MAST Table Access Protocol PanSTARRS 1 DR2 Demo#
This tutorial demonstrates how to use astroquery to access PanSTARRS 1 Data Release 2 via a Virtual Observatory standard Table Access Protocol (TAP) service at MAST, and work with the resultant data. It relies on Python 3 and astroquery, as well as some other common scientific packages.
Table of Contents#
TAP Service Introduction
Imports
Connecting to a TAP Service
Use Cases
Additional Resources
About This Notebook
TAP Service Introduction#
Table Access Protocol (TAP) services allow more direct and flexible access to astronomical data than the simpler types of IVOA standard data services. Queries are built with the SQL-like Astronomical Data Query Language (ADQL), and can include geographic / spatial queries as well as filtering on other characteristics of the data. This also allows the user fine-grained control over the returned columns, unlike the fixed set of coumns returned from cone, image, and spectral services.
For this example, we’ll be using the astropy affiliated PyVO client, which is interoperable with other valid TAP services, including those at MAST. PyVO documentation is available at ReadTheDocs: https://pyvo.readthedocs.io
We’ll be using PyVO to call the TAP service at MAST serving PanSTARRS 1 Data Release 2, now with individual detection information. The schema is described within the service, and we’ll show how to inspect it. The schema is also the same as the one available via the CasJobs interface, with an additional view added for the most common positional queries. CasJobs has its own copy of the schema documentation, which can be accessed through its own site: http://mastweb.stsci.edu/ps1casjobs/
Imports#
# Use the pyvo library as our client to the data service.
import pyvo as vo
# For resolving objects with tools from MAST
from astroquery.mast import Mast
# For handling ordinary astropy Tables in responses
from astropy.table import Table
# For displaying and manipulating some types of results
%matplotlib inline
import requests
import astropy
import numpy as np
import pylab
import time
import json
from matplotlib import pyplot as plt
# suppress unimportant unit warnings from many TAP services
import warnings
warnings.filterwarnings("ignore", module="astropy.io.votable.*")
warnings.filterwarnings("ignore", module="pyvo.utils.xml.elements")
Connecting to a TAP Service#
The PyVO library is able to connect to any TAP service, given the “base” URL as noted in metadata registry resources describing the service. This is the URL for the PanSTARRS 1 DR2 TAP service.
TAP_service = vo.dal.TAPService("https://mast.stsci.edu/vo-tap/api/v0.1/ps1dr2/")
TAP_service.describe()
Capability ivo://ivoa.net/std/TAP
Interface vs:ParamHTTP
https://mast.stsci.edu/vo-tap/api/v0.1/ps1dr2
Language ADQL
Output format application/x-votable+xml
Also available as votable
Output format text/csv;header=present
Also available as csv
Maximum size of resultsets
Default 100000 row
Maximum 100000 row
Capability ivo://ivoa.net/std/DALI#examples
Interface vr:WebBrowser
https://mast.stsci.edu/vo-tap/api/v0.1/ps1dr2/examples
List available tables#
TAP_tables = TAP_service.tables
for tablename in TAP_tables.keys():
if not "tap_schema" in tablename:
TAP_tables[tablename].describe()
print("Columns={}".format(sorted([k.name for k in TAP_tables[tablename].columns ])))
print("----")
dbo.ForcedMeanLensing
Contains the mean Kaiser et al. (1995) lensing parameters measured from the forced photometry of objects detected in stacked images on the individual single epoch data. References: Kaiser, N., Squires, G., and Broadhurst, T. 1995, ApJ, 449, 460.
Columns=['batchID', 'gLensObjShearE1', 'gLensObjShearE2', 'gLensObjShearX11', 'gLensObjShearX12', 'gLensObjShearX22', 'gLensObjSmearE1', 'gLensObjSmearE2', 'gLensObjSmearX11', 'gLensObjSmearX12', 'gLensObjSmearX22', 'gLensPSFShearE1', 'gLensPSFShearE2', 'gLensPSFShearX11', 'gLensPSFShearX12', 'gLensPSFShearX22', 'gLensPSFSmearE1', 'gLensPSFSmearE2', 'gLensPSFSmearX11', 'gLensPSFSmearX12', 'gLensPSFSmearX22', 'iLensObjShearE1', 'iLensObjShearE2', 'iLensObjShearX11', 'iLensObjShearX12', 'iLensObjShearX22', 'iLensObjSmearE1', 'iLensObjSmearE2', 'iLensObjSmearX11', 'iLensObjSmearX12', 'iLensObjSmearX22', 'iLensPSFShearE1', 'iLensPSFShearE2', 'iLensPSFShearX11', 'iLensPSFShearX12', 'iLensPSFShearX22', 'iLensPSFSmearE1', 'iLensPSFSmearE2', 'iLensPSFSmearX11', 'iLensPSFSmearX12', 'iLensPSFSmearX22', 'ippObjID', 'nDetections', 'objID', 'processingVersion', 'rLensObjShearE1', 'rLensObjShearE2', 'rLensObjShearX11', 'rLensObjShearX12', 'rLensObjShearX22', 'rLensObjSmearE1', 'rLensObjSmearE2', 'rLensObjSmearX11', 'rLensObjSmearX12', 'rLensObjSmearX22', 'rLensPSFShearE1', 'rLensPSFShearE2', 'rLensPSFShearX11', 'rLensPSFShearX12', 'rLensPSFShearX22', 'rLensPSFSmearE1', 'rLensPSFSmearE2', 'rLensPSFSmearX11', 'rLensPSFSmearX12', 'rLensPSFSmearX22', 'randomForcedObjID', 'uniquePspsFOid', 'yLensObjShearE1', 'yLensObjShearE2', 'yLensObjShearX11', 'yLensObjShearX12', 'yLensObjShearX22', 'yLensObjSmearE1', 'yLensObjSmearE2', 'yLensObjSmearX11', 'yLensObjSmearX12', 'yLensObjSmearX22', 'yLensPSFShearE1', 'yLensPSFShearE2', 'yLensPSFShearX11', 'yLensPSFShearX12', 'yLensPSFShearX22', 'yLensPSFSmearE1', 'yLensPSFSmearE2', 'yLensPSFSmearX11', 'yLensPSFSmearX12', 'yLensPSFSmearX22', 'zLensObjShearE1', 'zLensObjShearE2', 'zLensObjShearX11', 'zLensObjShearX12', 'zLensObjShearX22', 'zLensObjSmearE1', 'zLensObjSmearE2', 'zLensObjSmearX11', 'zLensObjSmearX12', 'zLensObjSmearX22', 'zLensPSFShearE1', 'zLensPSFShearE2', 'zLensPSFShearX11', 'zLensPSFShearX12', 'zLensPSFShearX22', 'zLensPSFSmearE1', 'zLensPSFSmearE2', 'zLensPSFSmearX11', 'zLensPSFSmearX12', 'zLensPSFSmearX22']
----
dbo.ForcedMeanObject
Contains the mean of single-epoch photometric information for sources detected in the stacked data, calculated as described in Magnier et al. (2013). The mean is calculated for detections associated into objects within a one arcsecond correlation radius. PSF, Kron (1980), and SDSS aperture R5 (r = 3.00 arcsec), R6 (r = 4.63 arcsec), and R7 (r = 7.43 arcsec) apertures (Stoughton 2002) magnitudes and statistics are listed for all filters. References: Kaiser, N., Squires, G., and Broadhurst, T. 1995, ApJ, 449, 460; Kron, R. G. 1980, ApJS, 43, 305; Magnier, E. A., Schlafly, E., Finkbeiner, D., et al. 2013, ApJS, 205, 20; Stoughton, C., Lupton, R. H., Bernardi, M., et al. 2002, AJ, 123, 485.
Columns=['batchID', 'gE1', 'gE2', 'gFApFlux', 'gFApFluxErr', 'gFApFluxStd', 'gFApMag', 'gFApMagErr', 'gFKronFlux', 'gFKronFluxErr', 'gFKronFluxStd', 'gFKronMag', 'gFKronMagErr', 'gFPSFFlux', 'gFPSFFluxErr', 'gFPSFFluxStd', 'gFPSFMag', 'gFPSFMagErr', 'gFlags', 'gFmeanMagR5', 'gFmeanMagR5Err', 'gFmeanMagR6', 'gFmeanMagR6Err', 'gFmeanMagR7', 'gFmeanMagR7Err', 'gFmeanflxR5', 'gFmeanflxR5Err', 'gFmeanflxR5Fill', 'gFmeanflxR5Std', 'gFmeanflxR6', 'gFmeanflxR6Err', 'gFmeanflxR6Fill', 'gFmeanflxR6Std', 'gFmeanflxR7', 'gFmeanflxR7Err', 'gFmeanflxR7Fill', 'gFmeanflxR7Std', 'gnIncApFlux', 'gnIncKronFlux', 'gnIncPSFFlux', 'gnIncR5', 'gnIncR6', 'gnIncR7', 'gnTotal', 'iE1', 'iE2', 'iFApFlux', 'iFApFluxErr', 'iFApFluxStd', 'iFApMag', 'iFApMagErr', 'iFKronFlux', 'iFKronFluxErr', 'iFKronFluxStd', 'iFKronMag', 'iFKronMagErr', 'iFPSFFlux', 'iFPSFFluxErr', 'iFPSFFluxStd', 'iFPSFMag', 'iFPSFMagErr', 'iFlags', 'iFmeanMagR5', 'iFmeanMagR5Err', 'iFmeanMagR6', 'iFmeanMagR6Err', 'iFmeanMagR7', 'iFmeanMagR7Err', 'iFmeanflxR5', 'iFmeanflxR5Err', 'iFmeanflxR5Fill', 'iFmeanflxR5Std', 'iFmeanflxR6', 'iFmeanflxR6Err', 'iFmeanflxR6Fill', 'iFmeanflxR6Std', 'iFmeanflxR7', 'iFmeanflxR7Err', 'iFmeanflxR7Fill', 'iFmeanflxR7Std', 'inIncApFlux', 'inIncKronFlux', 'inIncPSFFlux', 'inIncR5', 'inIncR6', 'inIncR7', 'inTotal', 'ippObjID', 'nDetections', 'objID', 'processingVersion', 'rE1', 'rE2', 'rFApFlux', 'rFApFluxErr', 'rFApFluxStd', 'rFApMag', 'rFApMagErr', 'rFKronFlux', 'rFKronFluxErr', 'rFKronFluxStd', 'rFKronMag', 'rFKronMagErr', 'rFPSFFlux', 'rFPSFFluxErr', 'rFPSFFluxStd', 'rFPSFMag', 'rFPSFMagErr', 'rFlags', 'rFmeanMagR5', 'rFmeanMagR5Err', 'rFmeanMagR6', 'rFmeanMagR6Err', 'rFmeanMagR7', 'rFmeanMagR7Err', 'rFmeanflxR5', 'rFmeanflxR5Err', 'rFmeanflxR5Fill', 'rFmeanflxR5Std', 'rFmeanflxR6', 'rFmeanflxR6Err', 'rFmeanflxR6Fill', 'rFmeanflxR6Std', 'rFmeanflxR7', 'rFmeanflxR7Err', 'rFmeanflxR7Fill', 'rFmeanflxR7Std', 'randomForcedObjID', 'rnIncApFlux', 'rnIncKronFlux', 'rnIncPSFFlux', 'rnIncR5', 'rnIncR6', 'rnIncR7', 'rnTotal', 'uniquePspsFOid', 'yE1', 'yE2', 'yFApFlux', 'yFApFluxErr', 'yFApFluxStd', 'yFApMag', 'yFApMagErr', 'yFKronFlux', 'yFKronFluxErr', 'yFKronFluxStd', 'yFKronMag', 'yFKronMagErr', 'yFPSFFlux', 'yFPSFFluxErr', 'yFPSFFluxStd', 'yFPSFMag', 'yFPSFMagErr', 'yFlags', 'yFmeanMagR5', 'yFmeanMagR5Err', 'yFmeanMagR6', 'yFmeanMagR6Err', 'yFmeanMagR7', 'yFmeanMagR7Err', 'yFmeanflxR5', 'yFmeanflxR5Err', 'yFmeanflxR5Fill', 'yFmeanflxR5Std', 'yFmeanflxR6', 'yFmeanflxR6Err', 'yFmeanflxR6Fill', 'yFmeanflxR6Std', 'yFmeanflxR7', 'yFmeanflxR7Err', 'yFmeanflxR7Fill', 'yFmeanflxR7Std', 'ynIncApFlux', 'ynIncKronFlux', 'ynIncPSFFlux', 'ynIncR5', 'ynIncR6', 'ynIncR7', 'ynTotal', 'zE1', 'zE2', 'zFApFlux', 'zFApFluxErr', 'zFApFluxStd', 'zFApMag', 'zFApMagErr', 'zFKronFlux', 'zFKronFluxErr', 'zFKronFluxStd', 'zFKronMag', 'zFKronMagErr', 'zFPSFFlux', 'zFPSFFluxErr', 'zFPSFFluxStd', 'zFPSFMag', 'zFPSFMagErr', 'zFlags', 'zFmeanMagR5', 'zFmeanMagR5Err', 'zFmeanMagR6', 'zFmeanMagR6Err', 'zFmeanMagR7', 'zFmeanMagR7Err', 'zFmeanflxR5', 'zFmeanflxR5Err', 'zFmeanflxR5Fill', 'zFmeanflxR5Std', 'zFmeanflxR6', 'zFmeanflxR6Err', 'zFmeanflxR6Fill', 'zFmeanflxR6Std', 'zFmeanflxR7', 'zFmeanflxR7Err', 'zFmeanflxR7Fill', 'zFmeanflxR7Std', 'znIncApFlux', 'znIncKronFlux', 'znIncPSFFlux', 'znIncR5', 'znIncR6', 'znIncR7', 'znTotal']
----
dbo.ForcedMeanObjectView
-- ObjectThin join ForcedMeanObject joined by objID colummn.
Columns=['b', 'batchID', 'beta', 'cx', 'cy', 'cz', 'decMean', 'decMeanErr', 'decStack', 'decStackErr', 'dvoRegionID', 'epochMean', 'gE1', 'gE2', 'gFApFlux', 'gFApFluxErr', 'gFApFluxStd', 'gFKronFlux', 'gFKronFluxErr', 'gFKronFluxStd', 'gFPSFFlux', 'gFPSFFluxErr', 'gFPSFFluxStd', 'gFlags', 'gFmeanflxR5', 'gFmeanflxR5Err', 'gFmeanflxR5Fill', 'gFmeanflxR5Std', 'gFmeanflxR6', 'gFmeanflxR6Err', 'gFmeanflxR6Fill', 'gFmeanflxR6Std', 'gFmeanflxR7', 'gFmeanflxR7Err', 'gFmeanflxR7Fill', 'gFmeanflxR7Std', 'gnIncApFlux', 'gnIncKronFlux', 'gnIncPSFFlux', 'gnIncR5', 'gnIncR6', 'gnIncR7', 'gnTotal', 'htmID', 'iE1', 'iE2', 'iFApFlux', 'iFApFluxErr', 'iFApFluxStd', 'iFKronFlux', 'iFKronFluxErr', 'iFKronFluxStd', 'iFPSFFlux', 'iFPSFFluxErr', 'iFPSFFluxStd', 'iFlags', 'iFmeanflxR5', 'iFmeanflxR5Err', 'iFmeanflxR5Fill', 'iFmeanflxR5Std', 'iFmeanflxR6', 'iFmeanflxR6Err', 'iFmeanflxR6Fill', 'iFmeanflxR6Std', 'iFmeanflxR7', 'iFmeanflxR7Err', 'iFmeanflxR7Fill', 'iFmeanflxR7Std', 'inIncApFlux', 'inIncKronFlux', 'inIncPSFFlux', 'inIncR5', 'inIncR6', 'inIncR7', 'inTotal', 'ippObjID', 'l', 'lambda', 'nDetections', 'nStackDetections', 'nStackObjectRows', 'ng', 'ni', 'nr', 'ny', 'nz', 'objAltName1', 'objAltName2', 'objAltName3', 'objID', 'objInfoFlag', 'objName', 'posMeanChisq', 'processingVersion', 'projectionID', 'qualityFlag', 'rE1', 'rE2', 'rFApFlux', 'rFApFluxErr', 'rFApFluxStd', 'rFKronFlux', 'rFKronFluxErr', 'rFKronFluxStd', 'rFPSFFlux', 'rFPSFFluxErr', 'rFPSFFluxStd', 'rFlags', 'rFmeanflxR5', 'rFmeanflxR5Err', 'rFmeanflxR5Fill', 'rFmeanflxR5Std', 'rFmeanflxR6', 'rFmeanflxR6Err', 'rFmeanflxR6Fill', 'rFmeanflxR6Std', 'rFmeanflxR7', 'rFmeanflxR7Err', 'rFmeanflxR7Fill', 'rFmeanflxR7Std', 'raMean', 'raMeanErr', 'raStack', 'raStackErr', 'randomID', 'rnIncApFlux', 'rnIncKronFlux', 'rnIncPSFFlux', 'rnIncR5', 'rnIncR6', 'rnIncR7', 'rnTotal', 'skyCellID', 'surveyID', 'tessID', 'uniquePspsOBid', 'yE1', 'yE2', 'yFApFlux', 'yFApFluxErr', 'yFApFluxStd', 'yFKronFlux', 'yFKronFluxErr', 'yFKronFluxStd', 'yFPSFFlux', 'yFPSFFluxErr', 'yFPSFFluxStd', 'yFlags', 'yFmeanflxR5', 'yFmeanflxR5Err', 'yFmeanflxR5Fill', 'yFmeanflxR5Std', 'yFmeanflxR6', 'yFmeanflxR6Err', 'yFmeanflxR6Fill', 'yFmeanflxR6Std', 'yFmeanflxR7', 'yFmeanflxR7Err', 'yFmeanflxR7Fill', 'yFmeanflxR7Std', 'ynIncApFlux', 'ynIncKronFlux', 'ynIncPSFFlux', 'ynIncR5', 'ynIncR6', 'ynIncR7', 'ynTotal', 'zE1', 'zE2', 'zFApFlux', 'zFApFluxErr', 'zFApFluxStd', 'zFKronFlux', 'zFKronFluxErr', 'zFKronFluxStd', 'zFPSFFlux', 'zFPSFFluxErr', 'zFPSFFluxStd', 'zFlags', 'zFmeanflxR5', 'zFmeanflxR5Err', 'zFmeanflxR5Fill', 'zFmeanflxR5Std', 'zFmeanflxR6', 'zFmeanflxR6Err', 'zFmeanflxR6Fill', 'zFmeanflxR6Std', 'zFmeanflxR7', 'zFmeanflxR7Err', 'zFmeanflxR7Fill', 'zFmeanflxR7Std', 'znIncApFlux', 'znIncKronFlux', 'znIncPSFFlux', 'znIncR5', 'znIncR6', 'znIncR7', 'znTotal', 'zoneID']
----
dbo.Detection
Contains single epoch photometry of individual detections from a single exposure. The identifiers connecting the detection back to the original image and to the object association are provided. PSF, aperture, and Kron (1980) photometry are included, along with sky and detector coordinate positions. References: Kron, R. G. 1980, ApJS, 43, 305.
Columns=['airMass', 'apFillF', 'apFlux', 'apFluxErr', 'apRadius', 'dec', 'decErr', 'detectID', 'dvoRegionID', 'expTime', 'extNSigma', 'filterID', 'imageID', 'infoFlag', 'infoFlag2', 'infoFlag3', 'ippDetectID', 'ippObjID', 'kronFlux', 'kronFluxErr', 'kronRad', 'momentM3C', 'momentM3S', 'momentM4C', 'momentM4S', 'momentR1', 'momentRH', 'momentXX', 'momentXY', 'momentYY', 'objID', 'obsTime', 'pltScale', 'posAngle', 'processingVersion', 'psfChiSq', 'psfCore', 'psfFlux', 'psfFluxErr', 'psfLikelihood', 'psfMajorFWHM', 'psfMinorFWHM', 'psfQf', 'psfQfPerfect', 'psfTheta', 'ra', 'raErr', 'randomDetID', 'sky', 'skyErr', 'surveyID', 'telluricExt', 'uniquePspsP2id', 'xPos', 'xPosErr', 'yPos', 'yPosErr', 'zp']
----
dbo.DetectionFlags
Contains information flag values for detections.
Columns=['"value"', 'description', 'hexadecimal', 'name']
----
dbo.DetectionFlags2
Contains information flag values for detections. References: Petrosian, V. 1976, ApJL, 209, L1.
Columns=['"value"', 'description', 'hexadecimal', 'name']
----
dbo.DetectionFlags3
Contains information flag values for detections. References: Kron, R. G. 1980, ApJS, 43, 305.
Columns=['"value"', 'description', 'hexadecimal', 'name']
----
dbo.DetectionObjectView
-- ObjectThin join Detection joined by objID column.
Columns=['airMass', 'apFillF', 'apFlux', 'apFluxErr', 'apRadius', 'b', 'batchID', 'beta', 'cx', 'cy', 'cz', 'dec', 'decErr', 'decMean', 'decMeanErr', 'decStack', 'decStackErr', 'detectID', 'dvoRegionID', 'epochMean', 'expTime', 'extNSigma', 'filterID', 'gFlags', 'gMeanApMag', 'gMeanApMagErr', 'gMeanApMagNpt', 'gMeanApMagStd', 'gMeanKronMag', 'gMeanKronMagErr', 'gMeanKronMagNpt', 'gMeanKronMagStd', 'gMeanPSFMag', 'gMeanPSFMagErr', 'gMeanPSFMagMax', 'gMeanPSFMagMin', 'gMeanPSFMagNpt', 'gMeanPSFMagStd', 'gQfPerfect', 'htmID', 'iFlags', 'iMeanApMag', 'iMeanApMagErr', 'iMeanApMagNpt', 'iMeanApMagStd', 'iMeanKronMag', 'iMeanKronMagErr', 'iMeanKronMagNpt', 'iMeanKronMagStd', 'iMeanPSFMag', 'iMeanPSFMagErr', 'iMeanPSFMagMax', 'iMeanPSFMagMin', 'iMeanPSFMagNpt', 'iMeanPSFMagStd', 'iQfPerfect', 'imageID', 'infoFlag', 'infoFlag2', 'infoFlag3', 'ippObjID', 'kronFlux', 'kronFluxErr', 'kronRad', 'l', 'lambda', 'momentM3C', 'momentM3S', 'momentM4C', 'momentM4S', 'momentR1', 'momentRH', 'momentXX', 'momentXY', 'momentYY', 'nDetections', 'nStackDetections', 'nStackObjectRows', 'ng', 'ni', 'nr', 'ny', 'nz', 'objAltName1', 'objAltName2', 'objAltName3', 'objID', 'objInfoFlag', 'objName', 'obsTime', 'pltScale', 'posAngle', 'posMeanChisq', 'processingVersion', 'projectionID', 'psfChiSq', 'psfCore', 'psfFlux', 'psfFluxErr', 'psfLikelihood', 'psfMajorFWHM', 'psfMinorFWHM', 'psfQf', 'psfQfPerfect', 'psfTheta', 'qualityFlag', 'rFlags', 'rMeanApMag', 'rMeanApMagErr', 'rMeanApMagNpt', 'rMeanApMagStd', 'rMeanKronMag', 'rMeanKronMagErr', 'rMeanKronMagNpt', 'rMeanKronMagStd', 'rMeanPSFMag', 'rMeanPSFMagErr', 'rMeanPSFMagMax', 'rMeanPSFMagMin', 'rMeanPSFMagNpt', 'rMeanPSFMagStd', 'rQfPerfect', 'ra', 'raErr', 'raMean', 'raMeanErr', 'raStack', 'raStackErr', 'randomDetID', 'randomID', 'sky', 'skyCellID', 'skyErr', 'surveyID', 'telluricExt', 'tessID', 'uniquePspsOBid', 'uniquePspsP2id', 'xPos', 'xPosErr', 'yFlags', 'yMeanApMag', 'yMeanApMagErr', 'yMeanApMagNpt', 'yMeanApMagStd', 'yMeanKronMag', 'yMeanKronMagErr', 'yMeanKronMagNpt', 'yMeanKronMagStd', 'yMeanPSFMag', 'yMeanPSFMagErr', 'yMeanPSFMagMax', 'yMeanPSFMagMin', 'yMeanPSFMagNpt', 'yMeanPSFMagStd', 'yPos', 'yPosErr', 'yQfPerfect', 'zFlags', 'zMeanApMag', 'zMeanApMagErr', 'zMeanApMagNpt', 'zMeanApMagStd', 'zMeanKronMag', 'zMeanKronMagErr', 'zMeanKronMagNpt', 'zMeanKronMagStd', 'zMeanPSFMag', 'zMeanPSFMagErr', 'zMeanPSFMagMax', 'zMeanPSFMagMin', 'zMeanPSFMagNpt', 'zMeanPSFMagStd', 'zQfPerfect', 'zoneID', 'zp']
----
dbo.DiffDetection
Contains the photometry of individual detections from a difference image. The identifiers connecting the detection back to the difference image and to the object association are provided. PSF, aperture, and Kron (1980) photometry are included, along with sky and detector coordinate positions. Statistics References: Kron, R. G. 1980, ApJS, 43, 305.
Columns=['DapFillF', 'DapFlux', 'DapFluxErr', 'DinfoFlag', 'DinfoFlag2', 'DinfoFlag3', 'DkronFlux', 'DkronFluxErr', 'DkronRad', 'DmomentR1', 'DmomentRH', 'DmomentXX', 'DmomentXY', 'DmomentYY', 'DpsfChiSq', 'DpsfCore', 'DpsfFlux', 'DpsfFluxErr', 'DpsfLikelihood', 'DpsfMajorFWHM', 'DpsfMinorFWHM', 'DpsfQf', 'DpsfQfPerfect', 'DpsfTheta', 'Dsky', 'DskyErr', 'airMass', 'ccdID', 'dec', 'decErr', 'diffDetID', 'diffFPosRatio', 'diffImageID', 'diffNPos', 'diffNPosAll', 'diffNPosMask', 'diffNPosRatio', 'diffNegDist', 'diffNegSN', 'diffObjID', 'diffPosDist', 'diffPosSN', 'dvoRegionID', 'expTime', 'filterID', 'fromPosImage', 'ippDetectID', 'ippObjID', 'obsTime', 'pltScale', 'posAngle', 'processingVersion', 'projectionID', 'ra', 'raErr', 'randomDiffID', 'skyCellID', 'surveyID', 'telluricExt', 'tessID', 'uniquePspsDFid', 'xPos', 'xPosChip', 'xPosErr', 'yPos', 'yPosChip', 'yPosErr', 'zp']
----
dbo.DiffDetEffMeta
Contains the detection efficiency information for a given individual difference image. Provides the number of recovered sources out of 500 injected sources and statistics about the magnitudes of the recovered sources for a range of magnitude offsets.
Columns=['counts01', 'counts02', 'counts03', 'counts04', 'counts05', 'counts06', 'counts07', 'counts08', 'counts09', 'counts10', 'counts11', 'counts12', 'counts13', 'diffImageID', 'diffMean01', 'diffMean02', 'diffMean03', 'diffMean04', 'diffMean05', 'diffMean06', 'diffMean07', 'diffMean08', 'diffMean09', 'diffMean10', 'diffMean11', 'diffMean12', 'diffMean13', 'diffStdev01', 'diffStdev02', 'diffStdev03', 'diffStdev04', 'diffStdev05', 'diffStdev06', 'diffStdev07', 'diffStdev08', 'diffStdev09', 'diffStdev10', 'diffStdev11', 'diffStdev12', 'diffStdev13', 'errMean01', 'errMean02', 'errMean03', 'errMean04', 'errMean05', 'errMean06', 'errMean07', 'errMean08', 'errMean09', 'errMean10', 'errMean11', 'errMean12', 'errMean13', 'magref', 'nInjected', 'offset01', 'offset02', 'offset03', 'offset04', 'offset05', 'offset06', 'offset07', 'offset08', 'offset09', 'offset10', 'offset11', 'offset12', 'offset13']
----
dbo.DiffDetObject
Contains the positional information for difference detection objects in a number of coordinate systems. The objects associate difference detections within a one arcsecond radius. The number of detections in each filter from is listed, along with maximum coverage fractions. References: Szalay, A. S., Gray, J., Fekete, G., et al. 2007, arXiv:cs/0701164.
Columns=['b', 'batchID', 'beta', 'cx', 'cy', 'cz', 'dec', 'diffObjAltName1', 'diffObjAltName2', 'diffObjAltName3', 'diffObjID', 'diffObjName', 'diffObjPSOName', 'diffObjPopularName', 'dvoRegionID', 'gQfPerfect', 'htmID', 'iQfPerfect', 'ippObjID', 'l', 'lambda', 'nDetections', 'ng', 'ni', 'nr', 'ny', 'nz', 'objInfoFlag', 'processingVersion', 'qualityFlag', 'rQfPerfect', 'ra', 'randomDiffObjID', 'surveyID', 'uniquePspsDOid', 'yQfPerfect', 'zQfPerfect', 'zoneID']
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dbo.DiffDetObjectView
-- DiffDetObject join DiffDetection joined by diffobjID column.
Columns=['DapFillF', 'DapFlux', 'DapFluxErr', 'DinfoFlag', 'DinfoFlag2', 'DinfoFlag3', 'DkronFlux', 'DkronFluxErr', 'DkronRad', 'DmomentR1', 'DmomentRH', 'DmomentXX', 'DmomentXY', 'DmomentYY', 'DpsfChiSq', 'DpsfCore', 'DpsfFlux', 'DpsfFluxErr', 'DpsfLikelihood', 'DpsfMajorFWHM', 'DpsfMinorFWHM', 'DpsfQf', 'DpsfQfPerfect', 'DpsfTheta', 'Dsky', 'DskyErr', 'airMass', 'b', 'batchID', 'beta', 'ccdID', 'cx', 'cy', 'cz', 'dec', 'decErr', 'diffFPosRatio', 'diffNPos', 'diffNPosAll', 'diffNPosMask', 'diffNPosRatio', 'diffNegDist', 'diffNegSN', 'diffObjAltName1', 'diffObjAltName2', 'diffObjAltName3', 'diffObjID', 'diffObjName', 'diffPosDist', 'diffPosSN', 'dvoRegionID', 'expTime', 'filterID', 'fromPosImage', 'gQfPerfect', 'htmID', 'iQfPerfect', 'ippObjID', 'l', 'lambda', 'nDetections', 'ng', 'ni', 'nr', 'ny', 'nz', 'objInfoFlag', 'obsTime', 'pltScale', 'posAngle', 'processingVersion', 'projectionID', 'qualityFlag', 'rQfPerfect', 'ra', 'raErr', 'randomDiffObjID', 'skyCellID', 'surveyID', 'telluricExt', 'tessID', 'uniquePspsDOid', 'xPos', 'xPosChip', 'xPosErr', 'yPos', 'yPosChip', 'yPosErr', 'yQfPerfect', 'zQfPerfect', 'zoneID', 'zp']
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dbo.DiffMeta
Contains metadata related to a difference image constructed by subtracting a stacked image from a single epoch image, or in the case of the MD Survey from a nightly stack (stack made from all exposures in a single filter in a single night). The astrometric calibration of the reference stack is listed.
Columns=['analysisVer', 'batchID', 'cdelt1', 'cdelt2', 'convolveMax', 'crpix1', 'crpix2', 'crval1', 'crval2', 'ctype1', 'ctype2', 'deconvolveMax', 'detectionThreshold', 'diffImageID', 'diffTypeID', 'expTime', 'filterID', 'frameID', 'ippDiffID', 'kernel', 'md5sum', 'mode', 'negImageID', 'normalization', 'numStamps', 'pc001001', 'pc001002', 'pc002001', 'pc002002', 'photoCalID', 'posImageID', 'processingVersion', 'projectionID', 'psfFWHM', 'psfModelID', 'psfTheta', 'psfWidMajor', 'psfWidMinor', 'skyCellID', 'stampDevMean', 'stampDevRMS', 'surveyID', 'tessID']
----
dbo.DiffToImage
Contains the mapping of which input images were used to construct a particular difference image.
Columns=['diffImageID', 'imageID']
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dbo.DiffType
Contains descriptions of the types of difference images constructed.
Columns=['description', 'diffTypeID', 'name']
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dbo.Filter
Contains a description of the optical filters used in the survey (Tonry et al 2012, ApJ 750, 99).
Columns=['filterID', 'filterType']
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dbo.FitModel
Contains descriptions of the models used in fitting detections in images, both PSF-like and extended galaxies. References: de Vaucouleurs, G. 1948, Annales d Astrophysique, 11, 247; Sersic, J. L. 1963, Boletin de la Asociacion Argentina de Astronomia La Plata Argentina, 6, 41.
Columns=['description', 'fitModelID', 'name', 'param1', 'param10', 'param2', 'param3', 'param4', 'param5', 'param6', 'param7', 'param8', 'param9']
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dbo.ForcedGalaxyModelView
-- ObjectThin join ForcedGalaxyShape joined by objID column.
Columns=['b', 'batchID', 'beta', 'cx', 'cy', 'cz', 'decMean', 'decMeanErr', 'decStack', 'decStackErr', 'dvoRegionID', 'epochMean', 'gGalChisq', 'gGalFlags', 'gGalIndex', 'gGalMag', 'gGalMagErr', 'gGalMajor', 'gGalMajorErr', 'gGalMinor', 'gGalMinorErr', 'gGalPhi', 'galModelType', 'gippDetectID', 'gstackImageID', 'htmID', 'iGalChisq', 'iGalFlags', 'iGalIndex', 'iGalMag', 'iGalMagErr', 'iGalMajor', 'iGalMajorErr', 'iGalMinor', 'iGalMinorErr', 'iGalPhi', 'iippDetectID', 'ippObjID', 'istackImageID', 'l', 'lambda', 'nDetections', 'nFilter', 'nStackDetections', 'nStackObjectRows', 'ng', 'ni', 'nr', 'ny', 'nz', 'objAltName1', 'objAltName2', 'objAltName3', 'objID', 'objInfoFlag', 'objName', 'posMeanChisq', 'processingVersion', 'projectionID', 'qualityFlag', 'rGalChisq', 'rGalFlags', 'rGalIndex', 'rGalMag', 'rGalMagErr', 'rGalMajor', 'rGalMajorErr', 'rGalMinor', 'rGalMinorErr', 'rGalPhi', 'raMean', 'raMeanErr', 'raStack', 'raStackErr', 'randomID', 'rippDetectID', 'rstackImageID', 'skyCellID', 'surveyID', 'tessID', 'uniquePspsOBid', 'yGalChisq', 'yGalFlags', 'yGalIndex', 'yGalMag', 'yGalMagErr', 'yGalMajor', 'yGalMajorErr', 'yGalMinor', 'yGalMinorErr', 'yGalPhi', 'yippDetectID', 'ystackImageID', 'zGalChisq', 'zGalFlags', 'zGalIndex', 'zGalMag', 'zGalMagErr', 'zGalMajor', 'zGalMajorErr', 'zGalMinor', 'zGalMinorErr', 'zGalPhi', 'zippDetectID', 'zoneID', 'zstackImageID']
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dbo.ForcedGalaxyShape
Contains the extended source galaxy shape parameters. All filters are matched into a single row. The positions, magnitudes, fluxes, and Sersic indices are inherited from their parent measurement in the StackModelFit tables, and are reproduced here for convenience. The major and minor axes and orientation are recalculated on a warp-by-warp basis from the best fit given these inherited properties. References: Sersic, J. L. 1963, Boletin de la Asociacion Argentina de Astronomia La Plata Argentina, 6, 41.
Columns=['gGalChisq', 'gGalFlags', 'gGalIndex', 'gGalMag', 'gGalMagErr', 'gGalMajor', 'gGalMajorErr', 'gGalMinor', 'gGalMinorErr', 'gGalPhi', 'galModelType', 'gippDetectID', 'gstackImageID', 'iGalChisq', 'iGalFlags', 'iGalIndex', 'iGalMag', 'iGalMagErr', 'iGalMajor', 'iGalMajorErr', 'iGalMinor', 'iGalMinorErr', 'iGalPhi', 'iippDetectID', 'ippObjID', 'istackImageID', 'nFilter', 'objID', 'rGalChisq', 'rGalFlags', 'rGalIndex', 'rGalMag', 'rGalMagErr', 'rGalMajor', 'rGalMajorErr', 'rGalMinor', 'rGalMinorErr', 'rGalPhi', 'randomForcedGalID', 'rippDetectID', 'rstackImageID', 'surveyID', 'uniquePspsFGid', 'yGalChisq', 'yGalFlags', 'yGalIndex', 'yGalMag', 'yGalMagErr', 'yGalMajor', 'yGalMajorErr', 'yGalMinor', 'yGalMinorErr', 'yGalPhi', 'yippDetectID', 'ystackImageID', 'zGalChisq', 'zGalFlags', 'zGalIndex', 'zGalMag', 'zGalMagErr', 'zGalMajor', 'zGalMajorErr', 'zGalMinor', 'zGalMinorErr', 'zGalPhi', 'zippDetectID', 'zstackImageID']
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dbo.ForcedGalaxyShapeFlags
Contains information flag values that define ForcedGalaxyShape chisq surface failures.
Columns=['"value"', 'description', 'hexadecimal', 'name']
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dbo.FrameMeta
Contains metadata related to an individual exposure. A ""Frame"" refers to the collection of all images obtained by the 60 OTA devices in the camera in a single exposure. The camera configuration, telescope pointing, observation time, and astrometric solution from the detector focal plane (L,M) to the sky (RA,Dec) is provided.
Columns=['airmass', 'analysisVer', 'batchID', 'cameraConfigID', 'cameraID', 'cdelt1', 'cdelt2', 'crpix1', 'crpix2', 'crval1', 'crval2', 'ctype1', 'ctype2', 'decBore', 'expStart', 'expTime', 'filterID', 'frameID', 'frameName', 'ippCamID', 'ippChipID', 'ippWarpID', 'md5sum', 'nOTA', 'nPhotoRef', 'pc001001', 'pc001002', 'pc002001', 'pc002002', 'pca1x0y2', 'pca1x0y3', 'pca1x1y1', 'pca1x1y2', 'pca1x2y0', 'pca1x2y1', 'pca1x3y0', 'pca2x0y2', 'pca2x0y3', 'pca2x1y1', 'pca2x1y2', 'pca2x2y0', 'pca2x2y1', 'pca2x3y0', 'photoScat', 'polyOrder', 'processingVersion', 'raBore', 'surveyID', 'telescopeID']
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dbo.ImageDetEffMeta
Contains the detection efficiency information for a given individual OTA image. Provides the number of recovered sources out of 500 injected fake source and statistics about the magnitudes of the recovered sources for a range of magnitude offsets.
Columns=['counts01', 'counts02', 'counts03', 'counts04', 'counts05', 'counts06', 'counts07', 'counts08', 'counts09', 'counts10', 'counts11', 'counts12', 'counts13', 'diffMean01', 'diffMean02', 'diffMean03', 'diffMean04', 'diffMean05', 'diffMean06', 'diffMean07', 'diffMean08', 'diffMean09', 'diffMean10', 'diffMean11', 'diffMean12', 'diffMean13', 'diffStdev01', 'diffStdev02', 'diffStdev03', 'diffStdev04', 'diffStdev05', 'diffStdev06', 'diffStdev07', 'diffStdev08', 'diffStdev09', 'diffStdev10', 'diffStdev11', 'diffStdev12', 'diffStdev13', 'errMean01', 'errMean02', 'errMean03', 'errMean04', 'errMean05', 'errMean06', 'errMean07', 'errMean08', 'errMean09', 'errMean10', 'errMean11', 'errMean12', 'errMean13', 'frameID', 'imageID', 'magref', 'nInjected', 'offset01', 'offset02', 'offset03', 'offset04', 'offset05', 'offset06', 'offset07', 'offset08', 'offset09', 'offset10', 'offset11', 'offset12', 'offset13']
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dbo.ImageFlags
Contains information flag values for images.
Columns=['"value"', 'description', 'hexadecimal', 'name']
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dbo.ImageMeta
Contains metadata related to an individual OTA image that comprises a portion of the full exposure. The characterization of the image quality, the detrends applied, and the astrometric solution from the raw pixels (X,Y) to the detector focal plane (L,M) is provided.
Columns=['apResid', 'astroScat', 'bias', 'biasScat', 'ccdID', 'cdelt1', 'cdelt2', 'crpix1', 'crpix2', 'crval1', 'crval2', 'ctype1', 'ctype2', 'dapResid', 'detectionThreshold', 'detectorID', 'detrend1', 'detrend2', 'detrend3', 'detrend4', 'detrend5', 'detrend6', 'detrend7', 'detrend8', 'filterID', 'frameID', 'imageID', 'momentM2C', 'momentM2S', 'momentM3', 'momentM4', 'momentMajor', 'momentMinor', 'nAstroRef', 'nAxis1', 'nAxis2', 'nDetect', 'nPhotoRef', 'pc001001', 'pc001002', 'pc002001', 'pc002002', 'pca1x0y2', 'pca1x0y3', 'pca1x1y1', 'pca1x1y2', 'pca1x2y0', 'pca1x2y1', 'pca1x3y0', 'pca2x0y2', 'pca2x0y3', 'pca2x1y1', 'pca2x1y2', 'pca2x2y0', 'pca2x2y1', 'pca2x3y0', 'photoCalID', 'photoScat', 'photoZero', 'polyOrder', 'processingVersion', 'psfFWHM', 'psfModelID', 'psfTheta', 'psfWidMajor', 'psfWidMinor', 'qaFlags', 'recalAstroScatX', 'recalAstroScatY', 'recalNAstroStars', 'recalNPhotoStars', 'recalphotoScat', 'sky', 'skyScat']
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dbo.MeanObject
Contains the mean photometric information for objects based on the single epoch data, calculated as described in Magnier et al (2013). To be included in this table, an object must be bright enough to have been detected at least once in an individual exposure. PSF, Kron (1980), and aperture magnitudes and statistics are listed for all filters. References: Kron, R. G. 1980, ApJS, 43, 305; Magnier, E. A., Schlafly, E., Finkbeiner, D., et al. 2013, ApJS, 205, 20.
Columns=['gFlags', 'gMeanApMag', 'gMeanApMagErr', 'gMeanApMagNpt', 'gMeanApMagStd', 'gMeanKronMag', 'gMeanKronMagErr', 'gMeanKronMagNpt', 'gMeanKronMagStd', 'gMeanPSFMag', 'gMeanPSFMagErr', 'gMeanPSFMagMax', 'gMeanPSFMagMin', 'gMeanPSFMagNpt', 'gMeanPSFMagStd', 'gQfPerfect', 'iFlags', 'iMeanApMag', 'iMeanApMagErr', 'iMeanApMagNpt', 'iMeanApMagStd', 'iMeanKronMag', 'iMeanKronMagErr', 'iMeanKronMagNpt', 'iMeanKronMagStd', 'iMeanPSFMag', 'iMeanPSFMagErr', 'iMeanPSFMagMax', 'iMeanPSFMagMin', 'iMeanPSFMagNpt', 'iMeanPSFMagStd', 'iQfPerfect', 'objID', 'rFlags', 'rMeanApMag', 'rMeanApMagErr', 'rMeanApMagNpt', 'rMeanApMagStd', 'rMeanKronMag', 'rMeanKronMagErr', 'rMeanKronMagNpt', 'rMeanKronMagStd', 'rMeanPSFMag', 'rMeanPSFMagErr', 'rMeanPSFMagMax', 'rMeanPSFMagMin', 'rMeanPSFMagNpt', 'rMeanPSFMagStd', 'rQfPerfect', 'uniquePspsOBid', 'yFlags', 'yMeanApMag', 'yMeanApMagErr', 'yMeanApMagNpt', 'yMeanApMagStd', 'yMeanKronMag', 'yMeanKronMagErr', 'yMeanKronMagNpt', 'yMeanKronMagStd', 'yMeanPSFMag', 'yMeanPSFMagErr', 'yMeanPSFMagMax', 'yMeanPSFMagMin', 'yMeanPSFMagNpt', 'yMeanPSFMagStd', 'yQfPerfect', 'zFlags', 'zMeanApMag', 'zMeanApMagErr', 'zMeanApMagNpt', 'zMeanApMagStd', 'zMeanKronMag', 'zMeanKronMagErr', 'zMeanKronMagNpt', 'zMeanKronMagStd', 'zMeanPSFMag', 'zMeanPSFMagErr', 'zMeanPSFMagMax', 'zMeanPSFMagMin', 'zMeanPSFMagNpt', 'zMeanPSFMagStd', 'zQfPerfect']
----
dbo.MeanObjectView
-- View based on a combination of the tables MeanObject and ObjectThin joined by objID colummn.
Columns=['b', 'batchID', 'beta', 'cx', 'cy', 'cz', 'decMean', 'decMeanErr', 'decStack', 'decStackErr', 'dvoRegionID', 'epochMean', 'gFlags', 'gMeanApMag', 'gMeanApMagErr', 'gMeanApMagNpt', 'gMeanApMagStd', 'gMeanKronMag', 'gMeanKronMagErr', 'gMeanKronMagNpt', 'gMeanKronMagStd', 'gMeanPSFMag', 'gMeanPSFMagErr', 'gMeanPSFMagMax', 'gMeanPSFMagMin', 'gMeanPSFMagNpt', 'gMeanPSFMagStd', 'gQfPerfect', 'htmID', 'iFlags', 'iMeanApMag', 'iMeanApMagErr', 'iMeanApMagNpt', 'iMeanApMagStd', 'iMeanKronMag', 'iMeanKronMagErr', 'iMeanKronMagNpt', 'iMeanKronMagStd', 'iMeanPSFMag', 'iMeanPSFMagErr', 'iMeanPSFMagMax', 'iMeanPSFMagMin', 'iMeanPSFMagNpt', 'iMeanPSFMagStd', 'iQfPerfect', 'ippObjID', 'l', 'lambda', 'nDetections', 'nStackDetections', 'nStackObjectRows', 'ng', 'ni', 'nr', 'ny', 'nz', 'objAltName1', 'objAltName2', 'objAltName3', 'objID', 'objInfoFlag', 'objName', 'posMeanChisq', 'processingVersion', 'projectionID', 'qualityFlag', 'rFlags', 'rMeanApMag', 'rMeanApMagErr', 'rMeanApMagNpt', 'rMeanApMagStd', 'rMeanKronMag', 'rMeanKronMagErr', 'rMeanKronMagNpt', 'rMeanKronMagStd', 'rMeanPSFMag', 'rMeanPSFMagErr', 'rMeanPSFMagMax', 'rMeanPSFMagMin', 'rMeanPSFMagNpt', 'rMeanPSFMagStd', 'rQfPerfect', 'raMean', 'raMeanErr', 'raStack', 'raStackErr', 'randomID', 'skyCellID', 'surveyID', 'tessID', 'uniquePspsOBid', 'yFlags', 'yMeanApMag', 'yMeanApMagErr', 'yMeanApMagNpt', 'yMeanApMagStd', 'yMeanKronMag', 'yMeanKronMagErr', 'yMeanKronMagNpt', 'yMeanKronMagStd', 'yMeanPSFMag', 'yMeanPSFMagErr', 'yMeanPSFMagMax', 'yMeanPSFMagMin', 'yMeanPSFMagNpt', 'yMeanPSFMagStd', 'yQfPerfect', 'zFlags', 'zMeanApMag', 'zMeanApMagErr', 'zMeanApMagNpt', 'zMeanApMagStd', 'zMeanKronMag', 'zMeanKronMagErr', 'zMeanKronMagNpt', 'zMeanKronMagStd', 'zMeanPSFMag', 'zMeanPSFMagErr', 'zMeanPSFMagMax', 'zMeanPSFMagMin', 'zMeanPSFMagNpt', 'zMeanPSFMagStd', 'zQfPerfect', 'zoneID']
----
dbo.ObjectFilterFlags
Contains information flags for the photometric calibration of an object.
Columns=['"value"', 'description', 'hexadecimal', 'name']
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dbo.ObjectInfoFlags
Contains information flag values for objects.
Columns=['"value"', 'description', 'hexadecimal', 'name']
----
dbo.ObjectQualityFlags
Contains information flag values that denote if an object is real or a possible false positive.
Columns=['"value"', 'description', 'hexadecimal', 'name']
----
dbo.ObjectThin
Contains the positional information for objects in a number of coordinate systems. The objects associate single epoch detections and the stacked detections within a one arcsecond radius. The mean position from the single epoch data is used as the basis for coordinates when available, or the position of an object in the stack when it is not. The right ascension and declination for both the stack and single epoch mean is provided. The number of detections in each filter from single epoch data is listed, along with which filters the object has a stack detection. References: Szalay, A. S., Gray, J., Fekete, G., et al. 2007, arXiv:cs/0701164.
Columns=['b', 'batchID', 'beta', 'cx', 'cy', 'cz', 'decMean', 'decMeanErr', 'decStack', 'decStackErr', 'dvoRegionID', 'epochMean', 'htmID', 'ippObjID', 'l', 'lambda', 'nDetections', 'nStackDetections', 'nStackObjectRows', 'ng', 'ni', 'nr', 'ny', 'nz', 'objAltName1', 'objAltName2', 'objAltName3', 'objID', 'objInfoFlag', 'objName', 'objPopularName', 'posMeanChisq', 'processingVersion', 'projectionID', 'qualityFlag', 'raMean', 'raMeanErr', 'raStack', 'raStackErr', 'randomID', 'skyCellID', 'surveyID', 'tessID', 'uniquePspsOBid', 'zoneID']
----
dbo.PhotoCal
Contains photometric calibration information for each filter and detector image combinations.
Columns=['AB', 'colorExtn', 'colorterm', 'extinction', 'filterID', 'orphanCalColor', 'orphanCalColorErr', 'photoCalID', 'photoCodeDesc', 'startDate', 'zeropoint']
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dbo.StackApFlx
Contains the unconvolved fluxes within the SDSS R5 (r = 3.00 arcsec), R6 (r = 4.63 arcsec), and R7 (r = 7.43 arcsec) apertures (Stoughton 2005). Convolved fluxes within these same apertures are also provided for images convolved to 6 sky pixels (1.5 arcsec) and 8 sky pixels (2.0 arcsec). All filters are matched into a single row. See StackObjectThin table for discussion of primary, secondary, and best detections. References: Stoughton, C., Lupton, R. H., Bernardi, M., et al. 2005, AJ, 123, 485.
Columns=['bestDetection', 'gc6flxR5', 'gc6flxR5Err', 'gc6flxR5Fill', 'gc6flxR5Std', 'gc6flxR6', 'gc6flxR6Err', 'gc6flxR6Fill', 'gc6flxR6Std', 'gc6flxR7', 'gc6flxR7Err', 'gc6flxR7Fill', 'gc6flxR7Std', 'gc8flxR5', 'gc8flxR5Err', 'gc8flxR5Fill', 'gc8flxR5Std', 'gc8flxR6', 'gc8flxR6Err', 'gc8flxR6Fill', 'gc8flxR6Std', 'gc8flxR7', 'gc8flxR7Err', 'gc8flxR7Fill', 'gc8flxR7Std', 'gflxR5', 'gflxR5Err', 'gflxR5Fill', 'gflxR5Std', 'gflxR6', 'gflxR6Err', 'gflxR6Fill', 'gflxR6Std', 'gflxR7', 'gflxR7Err', 'gflxR7Fill', 'gflxR7Std', 'gippDetectID', 'gstackDetectID', 'gstackImageID', 'ic6flxR5', 'ic6flxR5Err', 'ic6flxR5Fill', 'ic6flxR5Std', 'ic6flxR6', 'ic6flxR6Err', 'ic6flxR6Fill', 'ic6flxR6Std', 'ic6flxR7', 'ic6flxR7Err', 'ic6flxR7Fill', 'ic6flxR7Std', 'ic8flxR5', 'ic8flxR5Err', 'ic8flxR5Fill', 'ic8flxR5Std', 'ic8flxR6', 'ic8flxR6Err', 'ic8flxR6Fill', 'ic8flxR6Std', 'ic8flxR7', 'ic8flxR7Err', 'ic8flxR7Fill', 'ic8flxR7Std', 'iflxR5', 'iflxR5Err', 'iflxR5Fill', 'iflxR5Std', 'iflxR6', 'iflxR6Err', 'iflxR6Fill', 'iflxR6Std', 'iflxR7', 'iflxR7Err', 'iflxR7Fill', 'iflxR7Std', 'iippDetectID', 'ippObjID', 'istackDetectID', 'istackImageID', 'objID', 'primaryDetection', 'randomStackObjID', 'rc6flxR5', 'rc6flxR5Err', 'rc6flxR5Fill', 'rc6flxR5Std', 'rc6flxR6', 'rc6flxR6Err', 'rc6flxR6Fill', 'rc6flxR6Std', 'rc6flxR7', 'rc6flxR7Err', 'rc6flxR7Fill', 'rc6flxR7Std', 'rc8flxR5', 'rc8flxR5Err', 'rc8flxR5Fill', 'rc8flxR5Std', 'rc8flxR6', 'rc8flxR6Err', 'rc8flxR6Fill', 'rc8flxR6Std', 'rc8flxR7', 'rc8flxR7Err', 'rc8flxR7Fill', 'rc8flxR7Std', 'rflxR5', 'rflxR5Err', 'rflxR5Fill', 'rflxR5Std', 'rflxR6', 'rflxR6Err', 'rflxR6Fill', 'rflxR6Std', 'rflxR7', 'rflxR7Err', 'rflxR7Fill', 'rflxR7Std', 'rippDetectID', 'rstackDetectID', 'rstackImageID', 'uniquePspsSTid', 'yc6flxR5', 'yc6flxR5Err', 'yc6flxR5Fill', 'yc6flxR5Std', 'yc6flxR6', 'yc6flxR6Err', 'yc6flxR6Fill', 'yc6flxR6Std', 'yc6flxR7', 'yc6flxR7Err', 'yc6flxR7Fill', 'yc6flxR7Std', 'yc8flxR5', 'yc8flxR5Err', 'yc8flxR5Fill', 'yc8flxR5Std', 'yc8flxR6', 'yc8flxR6Err', 'yc8flxR6Fill', 'yc8flxR6Std', 'yc8flxR7', 'yc8flxR7Err', 'yc8flxR7Fill', 'yc8flxR7Std', 'yflxR5', 'yflxR5Err', 'yflxR5Fill', 'yflxR5Std', 'yflxR6', 'yflxR6Err', 'yflxR6Fill', 'yflxR6Std', 'yflxR7', 'yflxR7Err', 'yflxR7Fill', 'yflxR7Std', 'yippDetectID', 'ystackDetectID', 'ystackImageID', 'zc6flxR5', 'zc6flxR5Err', 'zc6flxR5Fill', 'zc6flxR5Std', 'zc6flxR6', 'zc6flxR6Err', 'zc6flxR6Fill', 'zc6flxR6Std', 'zc6flxR7', 'zc6flxR7Err', 'zc6flxR7Fill', 'zc6flxR7Std', 'zc8flxR5', 'zc8flxR5Err', 'zc8flxR5Fill', 'zc8flxR5Std', 'zc8flxR6', 'zc8flxR6Err', 'zc8flxR6Fill', 'zc8flxR6Std', 'zc8flxR7', 'zc8flxR7Err', 'zc8flxR7Fill', 'zc8flxR7Std', 'zflxR5', 'zflxR5Err', 'zflxR5Fill', 'zflxR5Std', 'zflxR6', 'zflxR6Err', 'zflxR6Fill', 'zflxR6Std', 'zflxR7', 'zflxR7Err', 'zflxR7Fill', 'zflxR7Std', 'zippDetectID', 'zstackDetectID', 'zstackImageID']
----
dbo.StackApFlxExGalCon6
Contains the fluxes within the SDSS R3 (r = 1.03 arcsec), R4 (r = 1.76 arcsec), R5 (r = 3.00 arcsec), R6 (r = 4.63 arcsec), R7 (r = 7.43 arcsec), R8 (r = 11.42 arcsec), R9 (r = 18.20 arcsec), R10 (r = 28.20 arcsec), and R11 (r = 44.21 arcsec) apertures (Stoughton 2005) for extended sources after the images have been convolved to a target of 6 sky pixels (1.5 arcsec). These measurements are only provided for objects in the extragalactic sky, i.e., they are not provided for objects in the Galactic plane because they are not useful in crowded areas. See StackObjectThin table for discussion of primary, secondary, and best detections. References: Stoughton, C., Lupton, R. H., Bernardi, M., et al. 2005, AJ, 123, 485.
Columns=['bestDetection', 'gc6flxR10', 'gc6flxR10Err', 'gc6flxR10Fill', 'gc6flxR10Std', 'gc6flxR11', 'gc6flxR11Err', 'gc6flxR11Fill', 'gc6flxR11Std', 'gc6flxR3', 'gc6flxR3Err', 'gc6flxR3Fill', 'gc6flxR3Std', 'gc6flxR4', 'gc6flxR4Err', 'gc6flxR4Fill', 'gc6flxR4Std', 'gc6flxR5', 'gc6flxR5Err', 'gc6flxR5Fill', 'gc6flxR5Std', 'gc6flxR6', 'gc6flxR6Err', 'gc6flxR6Fill', 'gc6flxR6Std', 'gc6flxR7', 'gc6flxR7Err', 'gc6flxR7Fill', 'gc6flxR7Std', 'gc6flxR8', 'gc6flxR8Err', 'gc6flxR8Fill', 'gc6flxR8Std', 'gc6flxR9', 'gc6flxR9Err', 'gc6flxR9Fill', 'gc6flxR9Std', 'gippDetectID', 'gstackDetectID', 'gstackImageID', 'ic6flxR10', 'ic6flxR10Err', 'ic6flxR10Fill', 'ic6flxR10Std', 'ic6flxR11', 'ic6flxR11Err', 'ic6flxR11Fill', 'ic6flxR11Std', 'ic6flxR3', 'ic6flxR3Err', 'ic6flxR3Fill', 'ic6flxR3Std', 'ic6flxR4', 'ic6flxR4Err', 'ic6flxR4Fill', 'ic6flxR4Std', 'ic6flxR5', 'ic6flxR5Err', 'ic6flxR5Fill', 'ic6flxR5Std', 'ic6flxR6', 'ic6flxR6Err', 'ic6flxR6Fill', 'ic6flxR6Std', 'ic6flxR7', 'ic6flxR7Err', 'ic6flxR7Fill', 'ic6flxR7Std', 'ic6flxR8', 'ic6flxR8Err', 'ic6flxR8Fill', 'ic6flxR8Std', 'ic6flxR9', 'ic6flxR9Err', 'ic6flxR9Fill', 'ic6flxR9Std', 'iippDetectID', 'ippObjID', 'istackDetectID', 'istackImageID', 'objID', 'primaryDetection', 'randomStackObjID', 'rc6flxR10', 'rc6flxR10Err', 'rc6flxR10Fill', 'rc6flxR10Std', 'rc6flxR11', 'rc6flxR11Err', 'rc6flxR11Fill', 'rc6flxR11Std', 'rc6flxR3', 'rc6flxR3Err', 'rc6flxR3Fill', 'rc6flxR3Std', 'rc6flxR4', 'rc6flxR4Err', 'rc6flxR4Fill', 'rc6flxR4Std', 'rc6flxR5', 'rc6flxR5Err', 'rc6flxR5Fill', 'rc6flxR5Std', 'rc6flxR6', 'rc6flxR6Err', 'rc6flxR6Fill', 'rc6flxR6Std', 'rc6flxR7', 'rc6flxR7Err', 'rc6flxR7Fill', 'rc6flxR7Std', 'rc6flxR8', 'rc6flxR8Err', 'rc6flxR8Fill', 'rc6flxR8Std', 'rc6flxR9', 'rc6flxR9Err', 'rc6flxR9Fill', 'rc6flxR9Std', 'rippDetectID', 'rstackDetectID', 'rstackImageID', 'uniquePspsSTid', 'yc6flxR10', 'yc6flxR10Err', 'yc6flxR10Fill', 'yc6flxR10Std', 'yc6flxR11', 'yc6flxR11Err', 'yc6flxR11Fill', 'yc6flxR11Std', 'yc6flxR3', 'yc6flxR3Err', 'yc6flxR3Fill', 'yc6flxR3Std', 'yc6flxR4', 'yc6flxR4Err', 'yc6flxR4Fill', 'yc6flxR4Std', 'yc6flxR5', 'yc6flxR5Err', 'yc6flxR5Fill', 'yc6flxR5Std', 'yc6flxR6', 'yc6flxR6Err', 'yc6flxR6Fill', 'yc6flxR6Std', 'yc6flxR7', 'yc6flxR7Err', 'yc6flxR7Fill', 'yc6flxR7Std', 'yc6flxR8', 'yc6flxR8Err', 'yc6flxR8Fill', 'yc6flxR8Std', 'yc6flxR9', 'yc6flxR9Err', 'yc6flxR9Fill', 'yc6flxR9Std', 'yippDetectID', 'ystackDetectID', 'ystackImageID', 'zc6flxR10', 'zc6flxR10Err', 'zc6flxR10Fill', 'zc6flxR10Std', 'zc6flxR11', 'zc6flxR11Err', 'zc6flxR11Fill', 'zc6flxR11Std', 'zc6flxR3', 'zc6flxR3Err', 'zc6flxR3Fill', 'zc6flxR3Std', 'zc6flxR4', 'zc6flxR4Err', 'zc6flxR4Fill', 'zc6flxR4Std', 'zc6flxR5', 'zc6flxR5Err', 'zc6flxR5Fill', 'zc6flxR5Std', 'zc6flxR6', 'zc6flxR6Err', 'zc6flxR6Fill', 'zc6flxR6Std', 'zc6flxR7', 'zc6flxR7Err', 'zc6flxR7Fill', 'zc6flxR7Std', 'zc6flxR8', 'zc6flxR8Err', 'zc6flxR8Fill', 'zc6flxR8Std', 'zc6flxR9', 'zc6flxR9Err', 'zc6flxR9Fill', 'zc6flxR9Std', 'zippDetectID', 'zstackDetectID', 'zstackImageID']
----
dbo.StackApFlxExGalCon6ObjectView
-- ObjectThin join StackApFlxExGalCon6 joined by objID column.
Columns=['b', 'batchID', 'bestDetection', 'beta', 'cx', 'cy', 'cz', 'decMean', 'decMeanErr', 'decStack', 'decStackErr', 'dvoRegionID', 'epochMean', 'gc6flxR10', 'gc6flxR10Err', 'gc6flxR10Fill', 'gc6flxR10Std', 'gc6flxR11', 'gc6flxR11Err', 'gc6flxR11Fill', 'gc6flxR11Std', 'gc6flxR3', 'gc6flxR3Err', 'gc6flxR3Fill', 'gc6flxR3Std', 'gc6flxR4', 'gc6flxR4Err', 'gc6flxR4Fill', 'gc6flxR4Std', 'gc6flxR5', 'gc6flxR5Err', 'gc6flxR5Fill', 'gc6flxR5Std', 'gc6flxR6', 'gc6flxR6Err', 'gc6flxR6Fill', 'gc6flxR6Std', 'gc6flxR7', 'gc6flxR7Err', 'gc6flxR7Fill', 'gc6flxR7Std', 'gc6flxR8', 'gc6flxR8Err', 'gc6flxR8Fill', 'gc6flxR8Std', 'gc6flxR9', 'gc6flxR9Err', 'gc6flxR9Fill', 'gc6flxR9Std', 'gippDetectID', 'gstackDetectID', 'gstackImageID', 'htmID', 'ic6flxR10', 'ic6flxR10Err', 'ic6flxR10Fill', 'ic6flxR10Std', 'ic6flxR11', 'ic6flxR11Err', 'ic6flxR11Fill', 'ic6flxR11Std', 'ic6flxR3', 'ic6flxR3Err', 'ic6flxR3Fill', 'ic6flxR3Std', 'ic6flxR4', 'ic6flxR4Err', 'ic6flxR4Fill', 'ic6flxR4Std', 'ic6flxR5', 'ic6flxR5Err', 'ic6flxR5Fill', 'ic6flxR5Std', 'ic6flxR6', 'ic6flxR6Err', 'ic6flxR6Fill', 'ic6flxR6Std', 'ic6flxR7', 'ic6flxR7Err', 'ic6flxR7Fill', 'ic6flxR7Std', 'ic6flxR8', 'ic6flxR8Err', 'ic6flxR8Fill', 'ic6flxR8Std', 'ic6flxR9', 'ic6flxR9Err', 'ic6flxR9Fill', 'ic6flxR9Std', 'iippDetectID', 'ippObjID', 'istackDetectID', 'istackImageID', 'l', 'lambda', 'nDetections', 'nStackDetections', 'nStackObjectRows', 'ng', 'ni', 'nr', 'ny', 'nz', 'objAltName1', 'objAltName2', 'objAltName3', 'objID', 'objInfoFlag', 'objName', 'posMeanChisq', 'primaryDetection', 'processingVersion', 'projectionID', 'qualityFlag', 'raMean', 'raMeanErr', 'raStack', 'raStackErr', 'randomID', 'rc6flxR10', 'rc6flxR10Err', 'rc6flxR10Fill', 'rc6flxR10Std', 'rc6flxR11', 'rc6flxR11Err', 'rc6flxR11Fill', 'rc6flxR11Std', 'rc6flxR3', 'rc6flxR3Err', 'rc6flxR3Fill', 'rc6flxR3Std', 'rc6flxR4', 'rc6flxR4Err', 'rc6flxR4Fill', 'rc6flxR4Std', 'rc6flxR5', 'rc6flxR5Err', 'rc6flxR5Fill', 'rc6flxR5Std', 'rc6flxR6', 'rc6flxR6Err', 'rc6flxR6Fill', 'rc6flxR6Std', 'rc6flxR7', 'rc6flxR7Err', 'rc6flxR7Fill', 'rc6flxR7Std', 'rc6flxR8', 'rc6flxR8Err', 'rc6flxR8Fill', 'rc6flxR8Std', 'rc6flxR9', 'rc6flxR9Err', 'rc6flxR9Fill', 'rc6flxR9Std', 'rippDetectID', 'rstackDetectID', 'rstackImageID', 'skyCellID', 'surveyID', 'tessID', 'uniquePspsOBid', 'yc6flxR10', 'yc6flxR10Err', 'yc6flxR10Fill', 'yc6flxR10Std', 'yc6flxR11', 'yc6flxR11Err', 'yc6flxR11Fill', 'yc6flxR11Std', 'yc6flxR3', 'yc6flxR3Err', 'yc6flxR3Fill', 'yc6flxR3Std', 'yc6flxR4', 'yc6flxR4Err', 'yc6flxR4Fill', 'yc6flxR4Std', 'yc6flxR5', 'yc6flxR5Err', 'yc6flxR5Fill', 'yc6flxR5Std', 'yc6flxR6', 'yc6flxR6Err', 'yc6flxR6Fill', 'yc6flxR6Std', 'yc6flxR7', 'yc6flxR7Err', 'yc6flxR7Fill', 'yc6flxR7Std', 'yc6flxR8', 'yc6flxR8Err', 'yc6flxR8Fill', 'yc6flxR8Std', 'yc6flxR9', 'yc6flxR9Err', 'yc6flxR9Fill', 'yc6flxR9Std', 'yippDetectID', 'ystackDetectID', 'ystackImageID', 'zc6flxR10', 'zc6flxR10Err', 'zc6flxR10Fill', 'zc6flxR10Std', 'zc6flxR11', 'zc6flxR11Err', 'zc6flxR11Fill', 'zc6flxR11Std', 'zc6flxR3', 'zc6flxR3Err', 'zc6flxR3Fill', 'zc6flxR3Std', 'zc6flxR4', 'zc6flxR4Err', 'zc6flxR4Fill', 'zc6flxR4Std', 'zc6flxR5', 'zc6flxR5Err', 'zc6flxR5Fill', 'zc6flxR5Std', 'zc6flxR6', 'zc6flxR6Err', 'zc6flxR6Fill', 'zc6flxR6Std', 'zc6flxR7', 'zc6flxR7Err', 'zc6flxR7Fill', 'zc6flxR7Std', 'zc6flxR8', 'zc6flxR8Err', 'zc6flxR8Fill', 'zc6flxR8Std', 'zc6flxR9', 'zc6flxR9Err', 'zc6flxR9Fill', 'zc6flxR9Std', 'zippDetectID', 'zoneID', 'zstackDetectID', 'zstackImageID']
----
dbo.StackApFlxExGalCon8
Contains the fluxes within the SDSS R3 (r = 1.03 arcsec), R4 (r = 1.76 arcsec), R5 (r = 3.00 arcsec), R6 (r = 4.63 arcsec), R7 (r = 7.43 arcsec), R8 (r = 11.42 arcsec), R9 (r = 18.20 arcsec), R10 (r = 28.20 arcsec), and R11 (r = 44.21 arcsec) apertures (Stoughton 2005) for extended sources after the images have been convolved to a target of 8 sky pixels (2.0 arcsec). These measurements are only provided for objects in the extragalactic sky, i.e., they are not provided for objects in the Galactic plane because they are not useful in crowded areas. See StackObjectThin table for discussion of primary, secondary, and best detections. References: Stoughton, C., Lupton, R. H., Bernardi, M., et al. 2005, AJ, 123, 485.
Columns=['bestDetection', 'gc8flxR10', 'gc8flxR10Err', 'gc8flxR10Fill', 'gc8flxR10Std', 'gc8flxR11', 'gc8flxR11Err', 'gc8flxR11Fill', 'gc8flxR11Std', 'gc8flxR3', 'gc8flxR3Err', 'gc8flxR3Fill', 'gc8flxR3Std', 'gc8flxR4', 'gc8flxR4Err', 'gc8flxR4Fill', 'gc8flxR4Std', 'gc8flxR5', 'gc8flxR5Err', 'gc8flxR5Fill', 'gc8flxR5Std', 'gc8flxR6', 'gc8flxR6Err', 'gc8flxR6Fill', 'gc8flxR6Std', 'gc8flxR7', 'gc8flxR7Err', 'gc8flxR7Fill', 'gc8flxR7Std', 'gc8flxR8', 'gc8flxR8Err', 'gc8flxR8Fill', 'gc8flxR8Std', 'gc8flxR9', 'gc8flxR9Err', 'gc8flxR9Fill', 'gc8flxR9Std', 'gippDetectID', 'gstackDetectID', 'gstackImageID', 'ic8flxR10', 'ic8flxR10Err', 'ic8flxR10Fill', 'ic8flxR10Std', 'ic8flxR11', 'ic8flxR11Err', 'ic8flxR11Fill', 'ic8flxR11Std', 'ic8flxR3', 'ic8flxR3Err', 'ic8flxR3Fill', 'ic8flxR3Std', 'ic8flxR4', 'ic8flxR4Err', 'ic8flxR4Fill', 'ic8flxR4Std', 'ic8flxR5', 'ic8flxR5Err', 'ic8flxR5Fill', 'ic8flxR5Std', 'ic8flxR6', 'ic8flxR6Err', 'ic8flxR6Fill', 'ic8flxR6Std', 'ic8flxR7', 'ic8flxR7Err', 'ic8flxR7Fill', 'ic8flxR7Std', 'ic8flxR8', 'ic8flxR8Err', 'ic8flxR8Fill', 'ic8flxR8Std', 'ic8flxR9', 'ic8flxR9Err', 'ic8flxR9Fill', 'ic8flxR9Std', 'iippDetectID', 'ippObjID', 'istackDetectID', 'istackImageID', 'objID', 'primaryDetection', 'randomStackObjID', 'rc8flxR10', 'rc8flxR10Err', 'rc8flxR10Fill', 'rc8flxR10Std', 'rc8flxR11', 'rc8flxR11Err', 'rc8flxR11Fill', 'rc8flxR11Std', 'rc8flxR3', 'rc8flxR3Err', 'rc8flxR3Fill', 'rc8flxR3Std', 'rc8flxR4', 'rc8flxR4Err', 'rc8flxR4Fill', 'rc8flxR4Std', 'rc8flxR5', 'rc8flxR5Err', 'rc8flxR5Fill', 'rc8flxR5Std', 'rc8flxR6', 'rc8flxR6Err', 'rc8flxR6Fill', 'rc8flxR6Std', 'rc8flxR7', 'rc8flxR7Err', 'rc8flxR7Fill', 'rc8flxR7Std', 'rc8flxR8', 'rc8flxR8Err', 'rc8flxR8Fill', 'rc8flxR8Std', 'rc8flxR9', 'rc8flxR9Err', 'rc8flxR9Fill', 'rc8flxR9Std', 'rippDetectID', 'rstackDetectID', 'rstackImageID', 'uniquePspsSTid', 'yc8flxR10', 'yc8flxR10Err', 'yc8flxR10Fill', 'yc8flxR10Std', 'yc8flxR11', 'yc8flxR11Err', 'yc8flxR11Fill', 'yc8flxR11Std', 'yc8flxR3', 'yc8flxR3Err', 'yc8flxR3Fill', 'yc8flxR3Std', 'yc8flxR4', 'yc8flxR4Err', 'yc8flxR4Fill', 'yc8flxR4Std', 'yc8flxR5', 'yc8flxR5Err', 'yc8flxR5Fill', 'yc8flxR5Std', 'yc8flxR6', 'yc8flxR6Err', 'yc8flxR6Fill', 'yc8flxR6Std', 'yc8flxR7', 'yc8flxR7Err', 'yc8flxR7Fill', 'yc8flxR7Std', 'yc8flxR8', 'yc8flxR8Err', 'yc8flxR8Fill', 'yc8flxR8Std', 'yc8flxR9', 'yc8flxR9Err', 'yc8flxR9Fill', 'yc8flxR9Std', 'yippDetectID', 'ystackDetectID', 'ystackImageID', 'zc8flxR10', 'zc8flxR10Err', 'zc8flxR10Fill', 'zc8flxR10Std', 'zc8flxR11', 'zc8flxR11Err', 'zc8flxR11Fill', 'zc8flxR11Std', 'zc8flxR3', 'zc8flxR3Err', 'zc8flxR3Fill', 'zc8flxR3Std', 'zc8flxR4', 'zc8flxR4Err', 'zc8flxR4Fill', 'zc8flxR4Std', 'zc8flxR5', 'zc8flxR5Err', 'zc8flxR5Fill', 'zc8flxR5Std', 'zc8flxR6', 'zc8flxR6Err', 'zc8flxR6Fill', 'zc8flxR6Std', 'zc8flxR7', 'zc8flxR7Err', 'zc8flxR7Fill', 'zc8flxR7Std', 'zc8flxR8', 'zc8flxR8Err', 'zc8flxR8Fill', 'zc8flxR8Std', 'zc8flxR9', 'zc8flxR9Err', 'zc8flxR9Fill', 'zc8flxR9Std', 'zippDetectID', 'zstackDetectID', 'zstackImageID']
----
dbo.StackApFlxExGalCon8ObjectView
-- ObjectThin join StackApFlxExGalCon8 joined by objID column.
Columns=['b', 'batchID', 'beta', 'cx', 'cy', 'cz', 'decMean', 'decMeanErr', 'decStack', 'decStackErr', 'dvoRegionID', 'epochMean', 'gc8flxR10', 'gc8flxR10Err', 'gc8flxR10Fill', 'gc8flxR10Std', 'gc8flxR11', 'gc8flxR11Err', 'gc8flxR11Fill', 'gc8flxR11Std', 'gc8flxR3', 'gc8flxR3Err', 'gc8flxR3Fill', 'gc8flxR3Std', 'gc8flxR4', 'gc8flxR4Err', 'gc8flxR4Fill', 'gc8flxR4Std', 'gc8flxR5', 'gc8flxR5Err', 'gc8flxR5Fill', 'gc8flxR5Std', 'gc8flxR6', 'gc8flxR6Err', 'gc8flxR6Fill', 'gc8flxR6Std', 'gc8flxR7', 'gc8flxR7Err', 'gc8flxR7Fill', 'gc8flxR7Std', 'gc8flxR8', 'gc8flxR8Err', 'gc8flxR8Fill', 'gc8flxR8Std', 'gc8flxR9', 'gc8flxR9Err', 'gc8flxR9Fill', 'gc8flxR9Std', 'gippDetectID', 'gstackDetectID', 'gstackImageID', 'htmID', 'ic8flxR10', 'ic8flxR10Err', 'ic8flxR10Fill', 'ic8flxR10Std', 'ic8flxR11', 'ic8flxR11Err', 'ic8flxR11Fill', 'ic8flxR11Std', 'ic8flxR3', 'ic8flxR3Err', 'ic8flxR3Fill', 'ic8flxR3Std', 'ic8flxR4', 'ic8flxR4Err', 'ic8flxR4Fill', 'ic8flxR4Std', 'ic8flxR5', 'ic8flxR5Err', 'ic8flxR5Fill', 'ic8flxR5Std', 'ic8flxR6', 'ic8flxR6Err', 'ic8flxR6Fill', 'ic8flxR6Std', 'ic8flxR7', 'ic8flxR7Err', 'ic8flxR7Fill', 'ic8flxR7Std', 'ic8flxR8', 'ic8flxR8Err', 'ic8flxR8Fill', 'ic8flxR8Std', 'ic8flxR9', 'ic8flxR9Err', 'ic8flxR9Fill', 'ic8flxR9Std', 'iippDetectID', 'ippObjID', 'istackDetectID', 'istackImageID', 'l', 'lambda', 'nDetections', 'nStackDetections', 'nStackObjectRows', 'ng', 'ni', 'nr', 'ny', 'nz', 'objAltName1', 'objAltName2', 'objAltName3', 'objID', 'objInfoFlag', 'objName', 'posMeanChisq', 'processingVersion', 'projectionID', 'qualityFlag', 'raMean', 'raMeanErr', 'raStack', 'raStackErr', 'randomID', 'rc8flxR10', 'rc8flxR10Err', 'rc8flxR10Fill', 'rc8flxR10Std', 'rc8flxR11', 'rc8flxR11Err', 'rc8flxR11Fill', 'rc8flxR11Std', 'rc8flxR3', 'rc8flxR3Err', 'rc8flxR3Fill', 'rc8flxR3Std', 'rc8flxR4', 'rc8flxR4Err', 'rc8flxR4Fill', 'rc8flxR4Std', 'rc8flxR5', 'rc8flxR5Err', 'rc8flxR5Fill', 'rc8flxR5Std', 'rc8flxR6', 'rc8flxR6Err', 'rc8flxR6Fill', 'rc8flxR6Std', 'rc8flxR7', 'rc8flxR7Err', 'rc8flxR7Fill', 'rc8flxR7Std', 'rc8flxR8', 'rc8flxR8Err', 'rc8flxR8Fill', 'rc8flxR8Std', 'rc8flxR9', 'rc8flxR9Err', 'rc8flxR9Fill', 'rc8flxR9Std', 'rippDetectID', 'rstackDetectID', 'rstackImageID', 'skyCellID', 'surveyID', 'tessID', 'uniquePspsOBid', 'yc8flxR10', 'yc8flxR10Err', 'yc8flxR10Fill', 'yc8flxR10Std', 'yc8flxR11', 'yc8flxR11Err', 'yc8flxR11Fill', 'yc8flxR11Std', 'yc8flxR3', 'yc8flxR3Err', 'yc8flxR3Fill', 'yc8flxR3Std', 'yc8flxR4', 'yc8flxR4Err', 'yc8flxR4Fill', 'yc8flxR4Std', 'yc8flxR5', 'yc8flxR5Err', 'yc8flxR5Fill', 'yc8flxR5Std', 'yc8flxR6', 'yc8flxR6Err', 'yc8flxR6Fill', 'yc8flxR6Std', 'yc8flxR7', 'yc8flxR7Err', 'yc8flxR7Fill', 'yc8flxR7Std', 'yc8flxR8', 'yc8flxR8Err', 'yc8flxR8Fill', 'yc8flxR8Std', 'yc8flxR9', 'yc8flxR9Err', 'yc8flxR9Fill', 'yc8flxR9Std', 'yippDetectID', 'ystackDetectID', 'ystackImageID', 'zc8flxR10', 'zc8flxR10Err', 'zc8flxR10Fill', 'zc8flxR10Std', 'zc8flxR11', 'zc8flxR11Err', 'zc8flxR11Fill', 'zc8flxR11Std', 'zc8flxR3', 'zc8flxR3Err', 'zc8flxR3Fill', 'zc8flxR3Std', 'zc8flxR4', 'zc8flxR4Err', 'zc8flxR4Fill', 'zc8flxR4Std', 'zc8flxR5', 'zc8flxR5Err', 'zc8flxR5Fill', 'zc8flxR5Std', 'zc8flxR6', 'zc8flxR6Err', 'zc8flxR6Fill', 'zc8flxR6Std', 'zc8flxR7', 'zc8flxR7Err', 'zc8flxR7Fill', 'zc8flxR7Std', 'zc8flxR8', 'zc8flxR8Err', 'zc8flxR8Fill', 'zc8flxR8Std', 'zc8flxR9', 'zc8flxR9Err', 'zc8flxR9Fill', 'zc8flxR9Std', 'zippDetectID', 'zoneID', 'zstackDetectID', 'zstackImageID']
----
dbo.StackApFlxExGalUnc
Contains the unconvolved fluxes within the SDSS R3 (r = 1.03 arcsec), R4 (r = 1.76 arcsec), R5 (r = 3.00 arcsec), R6 (r = 4.63 arcsec), R7 (r = 7.43 arcsec), R8 (r = 11.42 arcsec), R9 (r = 18.20 arcsec), R10 (r = 28.20 arcsec), and R11 (r = 44.21 arcsec) apertures (Stoughton 2005) for extended sources. These measurements are only provided for objects in the extragalactic sky, i.e., they are not provided for objects in the Galactic plane because they are not useful in crowded areas. See StackObjectThin table for discussion of primary, secondary, and best detections. References: Stoughton, C., Lupton, R. H., Bernardi, M., et al. 2005, AJ, 123, 485.
Columns=['bestDetection', 'gflxR10', 'gflxR10Err', 'gflxR10Fill', 'gflxR10Std', 'gflxR11', 'gflxR11Err', 'gflxR11Fill', 'gflxR11Std', 'gflxR3', 'gflxR3Err', 'gflxR3Fill', 'gflxR3Std', 'gflxR4', 'gflxR4Err', 'gflxR4Fill', 'gflxR4Std', 'gflxR5', 'gflxR5Err', 'gflxR5Fill', 'gflxR5Std', 'gflxR6', 'gflxR6Err', 'gflxR6Fill', 'gflxR6Std', 'gflxR7', 'gflxR7Err', 'gflxR7Fill', 'gflxR7Std', 'gflxR8', 'gflxR8Err', 'gflxR8Fill', 'gflxR8Std', 'gflxR9', 'gflxR9Err', 'gflxR9Fill', 'gflxR9Std', 'gippDetectID', 'gstackDetectID', 'gstackImageID', 'iflxR10', 'iflxR10Err', 'iflxR10Fill', 'iflxR10Std', 'iflxR11', 'iflxR11Err', 'iflxR11Fill', 'iflxR11Std', 'iflxR3', 'iflxR3Err', 'iflxR3Fill', 'iflxR3Std', 'iflxR4', 'iflxR4Err', 'iflxR4Fill', 'iflxR4Std', 'iflxR5', 'iflxR5Err', 'iflxR5Fill', 'iflxR5Std', 'iflxR6', 'iflxR6Err', 'iflxR6Fill', 'iflxR6Std', 'iflxR7', 'iflxR7Err', 'iflxR7Fill', 'iflxR7Std', 'iflxR8', 'iflxR8Err', 'iflxR8Fill', 'iflxR8Std', 'iflxR9', 'iflxR9Err', 'iflxR9Fill', 'iflxR9Std', 'iippDetectID', 'ippObjID', 'istackDetectID', 'istackImageID', 'objID', 'primaryDetection', 'randomStackObjID', 'rflxR10', 'rflxR10Err', 'rflxR10Fill', 'rflxR10Std', 'rflxR11', 'rflxR11Err', 'rflxR11Fill', 'rflxR11Std', 'rflxR3', 'rflxR3Err', 'rflxR3Fill', 'rflxR3Std', 'rflxR4', 'rflxR4Err', 'rflxR4Fill', 'rflxR4Std', 'rflxR5', 'rflxR5Err', 'rflxR5Fill', 'rflxR5Std', 'rflxR6', 'rflxR6Err', 'rflxR6Fill', 'rflxR6Std', 'rflxR7', 'rflxR7Err', 'rflxR7Fill', 'rflxR7Std', 'rflxR8', 'rflxR8Err', 'rflxR8Fill', 'rflxR8Std', 'rflxR9', 'rflxR9Err', 'rflxR9Fill', 'rflxR9Std', 'rippDetectID', 'rstackDetectID', 'rstackImageID', 'uniquePspsSTid', 'yflxR10', 'yflxR10Err', 'yflxR10Fill', 'yflxR10Std', 'yflxR11', 'yflxR11Err', 'yflxR11Fill', 'yflxR11Std', 'yflxR3', 'yflxR3Err', 'yflxR3Fill', 'yflxR3Std', 'yflxR4', 'yflxR4Err', 'yflxR4Fill', 'yflxR4Std', 'yflxR5', 'yflxR5Err', 'yflxR5Fill', 'yflxR5Std', 'yflxR6', 'yflxR6Err', 'yflxR6Fill', 'yflxR6Std', 'yflxR7', 'yflxR7Err', 'yflxR7Fill', 'yflxR7Std', 'yflxR8', 'yflxR8Err', 'yflxR8Fill', 'yflxR8Std', 'yflxR9', 'yflxR9Err', 'yflxR9Fill', 'yflxR9Std', 'yippDetectID', 'ystackDetectID', 'ystackImageID', 'zflxR10', 'zflxR10Err', 'zflxR10Fill', 'zflxR10Std', 'zflxR11', 'zflxR11Err', 'zflxR11Fill', 'zflxR11Std', 'zflxR3', 'zflxR3Err', 'zflxR3Fill', 'zflxR3Std', 'zflxR4', 'zflxR4Err', 'zflxR4Fill', 'zflxR4Std', 'zflxR5', 'zflxR5Err', 'zflxR5Fill', 'zflxR5Std', 'zflxR6', 'zflxR6Err', 'zflxR6Fill', 'zflxR6Std', 'zflxR7', 'zflxR7Err', 'zflxR7Fill', 'zflxR7Std', 'zflxR8', 'zflxR8Err', 'zflxR8Fill', 'zflxR8Std', 'zflxR9', 'zflxR9Err', 'zflxR9Fill', 'zflxR9Std', 'zippDetectID', 'zstackDetectID', 'zstackImageID']
----
dbo.StackApFlxExGalUncObjectView
-- ObjectThin join StackApFlxExGalUnc joined by objID column.
Columns=['b', 'batchID', 'bestDetection', 'beta', 'cx', 'cy', 'cz', 'decMean', 'decMeanErr', 'decStack', 'decStackErr', 'dvoRegionID', 'epochMean', 'gflxR10', 'gflxR10Err', 'gflxR10Fill', 'gflxR10Std', 'gflxR11', 'gflxR11Err', 'gflxR11Fill', 'gflxR11Std', 'gflxR3', 'gflxR3Err', 'gflxR3Fill', 'gflxR3Std', 'gflxR4', 'gflxR4Err', 'gflxR4Fill', 'gflxR4Std', 'gflxR5', 'gflxR5Err', 'gflxR5Fill', 'gflxR5Std', 'gflxR6', 'gflxR6Err', 'gflxR6Fill', 'gflxR6Std', 'gflxR7', 'gflxR7Err', 'gflxR7Fill', 'gflxR7Std', 'gflxR8', 'gflxR8Err', 'gflxR8Fill', 'gflxR8Std', 'gflxR9', 'gflxR9Err', 'gflxR9Fill', 'gflxR9Std', 'gippDetectID', 'gstackDetectID', 'gstackImageID', 'htmID', 'iflxR10', 'iflxR10Err', 'iflxR10Fill', 'iflxR10Std', 'iflxR11', 'iflxR11Err', 'iflxR11Fill', 'iflxR11Std', 'iflxR3', 'iflxR3Err', 'iflxR3Fill', 'iflxR3Std', 'iflxR4', 'iflxR4Err', 'iflxR4Fill', 'iflxR4Std', 'iflxR5', 'iflxR5Err', 'iflxR5Fill', 'iflxR5Std', 'iflxR6', 'iflxR6Err', 'iflxR6Fill', 'iflxR6Std', 'iflxR7', 'iflxR7Err', 'iflxR7Fill', 'iflxR7Std', 'iflxR8', 'iflxR8Err', 'iflxR8Fill', 'iflxR8Std', 'iflxR9', 'iflxR9Err', 'iflxR9Fill', 'iflxR9Std', 'iippDetectID', 'ippObjID', 'istackDetectID', 'istackImageID', 'l', 'lambda', 'nDetections', 'nStackDetections', 'nStackObjectRows', 'ng', 'ni', 'nr', 'ny', 'nz', 'objAltName1', 'objAltName2', 'objAltName3', 'objID', 'objInfoFlag', 'objName', 'posMeanChisq', 'primaryDetection', 'processingVersion', 'projectionID', 'qualityFlag', 'raMean', 'raMeanErr', 'raStack', 'raStackErr', 'randomID', 'randomStackObjID', 'rflxR10', 'rflxR10Err', 'rflxR10Fill', 'rflxR10Std', 'rflxR11', 'rflxR11Err', 'rflxR11Fill', 'rflxR11Std', 'rflxR3', 'rflxR3Err', 'rflxR3Fill', 'rflxR3Std', 'rflxR4', 'rflxR4Err', 'rflxR4Fill', 'rflxR4Std', 'rflxR5', 'rflxR5Err', 'rflxR5Fill', 'rflxR5Std', 'rflxR6', 'rflxR6Err', 'rflxR6Fill', 'rflxR6Std', 'rflxR7', 'rflxR7Err', 'rflxR7Fill', 'rflxR7Std', 'rflxR8', 'rflxR8Err', 'rflxR8Fill', 'rflxR8Std', 'rflxR9', 'rflxR9Err', 'rflxR9Fill', 'rflxR9Std', 'rippDetectID', 'rstackDetectID', 'rstackImageID', 'skyCellID', 'surveyID', 'tessID', 'uniquePspsOBid', 'yflxR10', 'yflxR10Err', 'yflxR10Fill', 'yflxR10Std', 'yflxR11', 'yflxR11Err', 'yflxR11Fill', 'yflxR11Std', 'yflxR3', 'yflxR3Err', 'yflxR3Fill', 'yflxR3Std', 'yflxR4', 'yflxR4Err', 'yflxR4Fill', 'yflxR4Std', 'yflxR5', 'yflxR5Err', 'yflxR5Fill', 'yflxR5Std', 'yflxR6', 'yflxR6Err', 'yflxR6Fill', 'yflxR6Std', 'yflxR7', 'yflxR7Err', 'yflxR7Fill', 'yflxR7Std', 'yflxR8', 'yflxR8Err', 'yflxR8Fill', 'yflxR8Std', 'yflxR9', 'yflxR9Err', 'yflxR9Fill', 'yflxR9Std', 'yippDetectID', 'ystackDetectID', 'ystackImageID', 'zflxR10', 'zflxR10Err', 'zflxR10Fill', 'zflxR10Std', 'zflxR11', 'zflxR11Err', 'zflxR11Fill', 'zflxR11Std', 'zflxR3', 'zflxR3Err', 'zflxR3Fill', 'zflxR3Std', 'zflxR4', 'zflxR4Err', 'zflxR4Fill', 'zflxR4Std', 'zflxR5', 'zflxR5Err', 'zflxR5Fill', 'zflxR5Std', 'zflxR6', 'zflxR6Err', 'zflxR6Fill', 'zflxR6Std', 'zflxR7', 'zflxR7Err', 'zflxR7Fill', 'zflxR7Std', 'zflxR8', 'zflxR8Err', 'zflxR8Fill', 'zflxR8Std', 'zflxR9', 'zflxR9Err', 'zflxR9Fill', 'zflxR9Std', 'zippDetectID', 'zoneID', 'zstackDetectID', 'zstackImageID']
----
dbo.StackApFlxObjectView
-- ObjectThin join StackApFlx joined by objID column.
Columns=['b', 'batchID', 'bestDetection', 'beta', 'cx', 'cy', 'cz', 'decMean', 'decMeanErr', 'decStack', 'decStackErr', 'dvoRegionID', 'epochMean', 'gc6flxR5', 'gc6flxR5Err', 'gc6flxR5Fill', 'gc6flxR5Std', 'gc6flxR6', 'gc6flxR6Err', 'gc6flxR6Fill', 'gc6flxR6Std', 'gc6flxR7', 'gc6flxR7Err', 'gc6flxR7Fill', 'gc6flxR7Std', 'gc8flxR5', 'gc8flxR5Err', 'gc8flxR5Fill', 'gc8flxR5Std', 'gc8flxR6', 'gc8flxR6Err', 'gc8flxR6Fill', 'gc8flxR6Std', 'gc8flxR7', 'gc8flxR7Err', 'gc8flxR7Fill', 'gc8flxR7Std', 'gflxR5', 'gflxR5Err', 'gflxR5Fill', 'gflxR5Std', 'gflxR6', 'gflxR6Err', 'gflxR6Fill', 'gflxR6Std', 'gflxR7', 'gflxR7Err', 'gflxR7Fill', 'gflxR7Std', 'gippDetectID', 'gstackDetectID', 'gstackImageID', 'htmID', 'ic6flxR5', 'ic6flxR5Err', 'ic6flxR5Fill', 'ic6flxR5Std', 'ic6flxR6', 'ic6flxR6Err', 'ic6flxR6Fill', 'ic6flxR6Std', 'ic6flxR7', 'ic6flxR7Err', 'ic6flxR7Fill', 'ic6flxR7Std', 'ic8flxR5', 'ic8flxR5Err', 'ic8flxR5Fill', 'ic8flxR5Std', 'ic8flxR6', 'ic8flxR6Err', 'ic8flxR6Fill', 'ic8flxR6Std', 'ic8flxR7', 'ic8flxR7Err', 'ic8flxR7Fill', 'ic8flxR7Std', 'iflxR5', 'iflxR5Err', 'iflxR5Fill', 'iflxR5Std', 'iflxR6', 'iflxR6Err', 'iflxR6Fill', 'iflxR6Std', 'iflxR7', 'iflxR7Err', 'iflxR7Fill', 'iflxR7Std', 'iippDetectID', 'ippObjID', 'istackDetectID', 'istackImageID', 'l', 'lambda', 'nDetections', 'nStackDetections', 'nStackObjectRows', 'ng', 'ni', 'nr', 'ny', 'nz', 'objAltName1', 'objAltName2', 'objAltName3', 'objID', 'objInfoFlag', 'objName', 'posMeanChisq', 'processingVersion', 'projectionID', 'qualityFlag', 'raMean', 'raMeanErr', 'raStack', 'raStackErr', 'randomID', 'rc6flxR5', 'rc6flxR5Err', 'rc6flxR5Fill', 'rc6flxR5Std', 'rc6flxR6', 'rc6flxR6Err', 'rc6flxR6Fill', 'rc6flxR6Std', 'rc6flxR7', 'rc6flxR7Err', 'rc6flxR7Fill', 'rc6flxR7Std', 'rc8flxR5', 'rc8flxR5Err', 'rc8flxR5Fill', 'rc8flxR5Std', 'rc8flxR6', 'rc8flxR6Err', 'rc8flxR6Fill', 'rc8flxR6Std', 'rc8flxR7', 'rc8flxR7Err', 'rc8flxR7Fill', 'rc8flxR7Std', 'rflxR5', 'rflxR5Err', 'rflxR5Fill', 'rflxR5Std', 'rflxR6', 'rflxR6Err', 'rflxR6Fill', 'rflxR6Std', 'rflxR7', 'rflxR7Err', 'rflxR7Fill', 'rflxR7Std', 'rippDetectID', 'rstackDetectID', 'rstackImageID', 'skyCellID', 'surveyID', 'tessID', 'uniquePspsOBid', 'yc6flxR5', 'yc6flxR5Err', 'yc6flxR5Fill', 'yc6flxR5Std', 'yc6flxR6', 'yc6flxR6Err', 'yc6flxR6Fill', 'yc6flxR6Std', 'yc6flxR7', 'yc6flxR7Err', 'yc6flxR7Fill', 'yc6flxR7Std', 'yc8flxR5', 'yc8flxR5Err', 'yc8flxR5Fill', 'yc8flxR5Std', 'yc8flxR6', 'yc8flxR6Err', 'yc8flxR6Fill', 'yc8flxR6Std', 'yc8flxR7', 'yc8flxR7Err', 'yc8flxR7Fill', 'yc8flxR7Std', 'yflxR5', 'yflxR5Err', 'yflxR5Fill', 'yflxR5Std', 'yflxR6', 'yflxR6Err', 'yflxR6Fill', 'yflxR6Std', 'yflxR7', 'yflxR7Err', 'yflxR7Fill', 'yflxR7Std', 'yippDetectID', 'ystackDetectID', 'ystackImageID', 'zc6flxR5', 'zc6flxR5Err', 'zc6flxR5Fill', 'zc6flxR5Std', 'zc6flxR6', 'zc6flxR6Err', 'zc6flxR6Fill', 'zc6flxR6Std', 'zc6flxR7', 'zc6flxR7Err', 'zc6flxR7Fill', 'zc6flxR7Std', 'zc8flxR5', 'zc8flxR5Err', 'zc8flxR5Fill', 'zc8flxR5Std', 'zc8flxR6', 'zc8flxR6Err', 'zc8flxR6Fill', 'zc8flxR6Std', 'zc8flxR7', 'zc8flxR7Err', 'zc8flxR7Fill', 'zc8flxR7Std', 'zflxR5', 'zflxR5Err', 'zflxR5Fill', 'zflxR5Std', 'zflxR6', 'zflxR6Err', 'zflxR6Fill', 'zflxR6Std', 'zflxR7', 'zflxR7Err', 'zflxR7Fill', 'zflxR7Std', 'zippDetectID', 'zoneID', 'zstackDetectID', 'zstackImageID']
----
dbo.StackDetEffMeta
Contains the detection efficiency information for a given stacked image. Provides the number of recovered sources out of 500 injected sources for each magnitude bin and statistics about the magnitudes of the recovered sources for a range of magnitude offsets.
Columns=['counts01', 'counts02', 'counts03', 'counts04', 'counts05', 'counts06', 'counts07', 'counts08', 'counts09', 'counts10', 'counts11', 'counts12', 'counts13', 'diffMean01', 'diffMean02', 'diffMean03', 'diffMean04', 'diffMean05', 'diffMean06', 'diffMean07', 'diffMean08', 'diffMean09', 'diffMean10', 'diffMean11', 'diffMean12', 'diffMean13', 'diffStdev01', 'diffStdev02', 'diffStdev03', 'diffStdev04', 'diffStdev05', 'diffStdev06', 'diffStdev07', 'diffStdev08', 'diffStdev09', 'diffStdev10', 'diffStdev11', 'diffStdev12', 'diffStdev13', 'errMean01', 'errMean02', 'errMean03', 'errMean04', 'errMean05', 'errMean06', 'errMean07', 'errMean08', 'errMean09', 'errMean10', 'errMean11', 'errMean12', 'errMean13', 'magref', 'nInjected', 'offset01', 'offset02', 'offset03', 'offset04', 'offset05', 'offset06', 'offset07', 'offset08', 'offset09', 'offset10', 'offset11', 'offset12', 'offset13', 'stackImageID']
----
dbo.StackMeta
Contains the metadata describing the stacked image produced from the combination of a set of single epoch exposures. The nature of the stack is given by the StackTypeID. The astrometric and photometric calibration of the stacked image are listed.
Columns=['analysisVer', 'astroScat', 'batchID', 'cdelt1', 'cdelt2', 'crpix1', 'crpix2', 'crval1', 'crval2', 'ctype1', 'ctype2', 'detectionThreshold', 'expTime', 'filterID', 'md5sum', 'nAstroRef', 'nP2Images', 'nPhotoRef', 'pc001001', 'pc001002', 'pc002001', 'pc002002', 'photoCalID', 'photoScat', 'photoZero', 'processingVersion', 'projectionID', 'psfFWHM', 'psfModelID', 'psfTheta', 'psfWidMajor', 'psfWidMinor', 'recalAstroScatX', 'recalAstroScatY', 'recalNAstroStars', 'recalNPhotoStars', 'recalphotoScat', 'skyCellID', 'stackImageID', 'stackTypeID', 'surveyID', 'tessID']
----
dbo.StackModelFitDeV
Contains the de Vaucouleurs (1948) fit parameters to extended sources. See StackObjectThin table for discussion of primary, secondary, and best detections. References: de Vaucouleurs, G. 1948, Annales d Astrophysique, 11, 247.
Columns=['bestDetection', 'gDeVAb', 'gDeVAbErr', 'gDeVChisq', 'gDeVDec', 'gDeVDecErr', 'gDeVMag', 'gDeVMagErr', 'gDeVPhi', 'gDeVPhiErr', 'gDeVRa', 'gDeVRaErr', 'gDeVRadius', 'gDeVRadiusErr', 'gippDetectID', 'gstackDetectID', 'gstackImageID', 'iDeVAb', 'iDeVAbErr', 'iDeVChisq', 'iDeVDec', 'iDeVDecErr', 'iDeVMag', 'iDeVMagErr', 'iDeVPhi', 'iDeVPhiErr', 'iDeVRa', 'iDeVRaErr', 'iDeVRadius', 'iDeVRadiusErr', 'iippDetectID', 'ippObjID', 'istackDetectID', 'istackImageID', 'objID', 'primaryDetection', 'rDeVAb', 'rDeVAbErr', 'rDeVChisq', 'rDeVDec', 'rDeVDecErr', 'rDeVMag', 'rDeVMagErr', 'rDeVPhi', 'rDeVPhiErr', 'rDeVRa', 'rDeVRaErr', 'rDeVRadius', 'rDeVRadiusErr', 'randomStackObjID', 'rippDetectID', 'rstackDetectID', 'rstackImageID', 'uniquePspsSTid', 'yDeVAb', 'yDeVAbErr', 'yDeVChisq', 'yDeVDec', 'yDeVDecErr', 'yDeVMag', 'yDeVMagErr', 'yDeVPhi', 'yDeVPhiErr', 'yDeVRa', 'yDeVRaErr', 'yDeVRadius', 'yDeVRadiusErr', 'yippDetectID', 'ystackDetectID', 'ystackImageID', 'zDeVAb', 'zDeVAbErr', 'zDeVChisq', 'zDeVDec', 'zDeVDecErr', 'zDeVMag', 'zDeVMagErr', 'zDeVPhi', 'zDeVPhiErr', 'zDeVRa', 'zDeVRaErr', 'zDeVRadius', 'zDeVRadiusErr', 'zippDetectID', 'zstackDetectID', 'zstackImageID']
----
dbo.StackModelFitDeVObjectView
-- ObjectThin join StackModelFitDeV joined by objID column.
Columns=['b', 'batchID', 'bestDetection', 'beta', 'cx', 'cy', 'cz', 'decMean', 'decMeanErr', 'decStack', 'decStackErr', 'dvoRegionID', 'epochMean', 'gDeVAb', 'gDeVAbErr', 'gDeVChisq', 'gDeVDec', 'gDeVDecErr', 'gDeVMag', 'gDeVMagErr', 'gDeVPhi', 'gDeVPhiErr', 'gDeVRa', 'gDeVRaErr', 'gDeVRadius', 'gDeVRadiusErr', 'gippDetectID', 'gstackDetectID', 'gstackImageID', 'htmID', 'iDeVAb', 'iDeVAbErr', 'iDeVChisq', 'iDeVDec', 'iDeVDecErr', 'iDeVMag', 'iDeVMagErr', 'iDeVPhi', 'iDeVPhiErr', 'iDeVRa', 'iDeVRaErr', 'iDeVRadius', 'iDeVRadiusErr', 'iippDetectID', 'ippObjID', 'istackDetectID', 'istackImageID', 'l', 'lambda', 'nDetections', 'nStackDetections', 'nStackObjectRows', 'ng', 'ni', 'nr', 'ny', 'nz', 'objAltName1', 'objAltName2', 'objAltName3', 'objID', 'objInfoFlag', 'objName', 'posMeanChisq', 'primaryDetection', 'processingVersion', 'projectionID', 'qualityFlag', 'rDeVAb', 'rDeVAbErr', 'rDeVChisq', 'rDeVDec', 'rDeVDecErr', 'rDeVMag', 'rDeVMagErr', 'rDeVPhi', 'rDeVPhiErr', 'rDeVRa', 'rDeVRaErr', 'rDeVRadius', 'rDeVRadiusErr', 'raMean', 'raMeanErr', 'raStack', 'raStackErr', 'randomID', 'rippDetectID', 'rstackDetectID', 'rstackImageID', 'skyCellID', 'surveyID', 'tessID', 'uniquePspsOBid', 'yDeVAb', 'yDeVAbErr', 'yDeVChisq', 'yDeVDec', 'yDeVDecErr', 'yDeVMag', 'yDeVMagErr', 'yDeVPhi', 'yDeVPhiErr', 'yDeVRa', 'yDeVRaErr', 'yDeVRadius', 'yDeVRadiusErr', 'yippDetectID', 'ystackDetectID', 'ystackImageID', 'zDeVAb', 'zDeVAbErr', 'zDeVChisq', 'zDeVDec', 'zDeVDecErr', 'zDeVMag', 'zDeVMagErr', 'zDeVPhi', 'zDeVPhiErr', 'zDeVRa', 'zDeVRaErr', 'zDeVRadius', 'zDeVRadiusErr', 'zippDetectID', 'zoneID', 'zstackDetectID', 'zstackImageID']
----
dbo.StackModelFitExp
Contains the exponential fit parameters to extended sources. See StackObjectThin table for discussion of primary, secondary, and best detections.
Columns=['bestDetection', 'gExpAb', 'gExpAbErr', 'gExpChisq', 'gExpDec', 'gExpDecErr', 'gExpMag', 'gExpMagErr', 'gExpPhi', 'gExpPhiErr', 'gExpRa', 'gExpRaErr', 'gExpRadius', 'gExpRadiusErr', 'gippDetectID', 'gstackDetectID', 'gstackImageID', 'iExpAb', 'iExpAbErr', 'iExpChisq', 'iExpDec', 'iExpDecErr', 'iExpMag', 'iExpMagErr', 'iExpPhi', 'iExpPhiErr', 'iExpRa', 'iExpRaErr', 'iExpRadius', 'iExpRadiusErr', 'iippDetectID', 'ippObjID', 'istackDetectID', 'istackImageID', 'objID', 'primaryDetection', 'rExpAb', 'rExpAbErr', 'rExpChisq', 'rExpDec', 'rExpDecErr', 'rExpMag', 'rExpMagErr', 'rExpPhi', 'rExpPhiErr', 'rExpRa', 'rExpRaErr', 'rExpRadius', 'rExpRadiusErr', 'randomStackObjID', 'rippDetectID', 'rstackDetectID', 'rstackImageID', 'uniquePspsSTid', 'yExpAb', 'yExpAbErr', 'yExpChisq', 'yExpDec', 'yExpDecErr', 'yExpMag', 'yExpMagErr', 'yExpPhi', 'yExpPhiErr', 'yExpRa', 'yExpRaErr', 'yExpRadius', 'yExpRadiusErr', 'yippDetectID', 'ystackDetectID', 'ystackImageID', 'zExpAb', 'zExpAbErr', 'zExpChisq', 'zExpDec', 'zExpDecErr', 'zExpMag', 'zExpMagErr', 'zExpPhi', 'zExpPhiErr', 'zExpRa', 'zExpRaErr', 'zExpRadius', 'zExpRadiusErr', 'zippDetectID', 'zstackDetectID', 'zstackImageID']
----
dbo.StackModelFitExpObjectView
-- ObjectThin join StackModelFitExp joined by objID column.
Columns=['b', 'batchID', 'bestDetection', 'beta', 'cx', 'cy', 'cz', 'decMean', 'decMeanErr', 'decStack', 'decStackErr', 'dvoRegionID', 'epochMean', 'gExpAb', 'gExpAbErr', 'gExpChisq', 'gExpDec', 'gExpDecErr', 'gExpMag', 'gExpMagErr', 'gExpPhi', 'gExpPhiErr', 'gExpRa', 'gExpRaErr', 'gExpRadius', 'gExpRadiusErr', 'gippDetectID', 'gstackDetectID', 'gstackImageID', 'htmID', 'iExpAb', 'iExpAbErr', 'iExpChisq', 'iExpDec', 'iExpDecErr', 'iExpMag', 'iExpMagErr', 'iExpPhi', 'iExpPhiErr', 'iExpRa', 'iExpRaErr', 'iExpRadius', 'iExpRadiusErr', 'iippDetectID', 'ippObjID', 'istackDetectID', 'istackImageID', 'l', 'lambda', 'nDetections', 'nStackDetections', 'nStackObjectRows', 'ng', 'ni', 'nr', 'ny', 'nz', 'objAltName1', 'objAltName2', 'objAltName3', 'objID', 'objInfoFlag', 'objName', 'posMeanChisq', 'primaryDetection', 'processingVersion', 'projectionID', 'qualityFlag', 'rExpAb', 'rExpAbErr', 'rExpChisq', 'rExpDec', 'rExpDecErr', 'rExpMag', 'rExpMagErr', 'rExpPhi', 'rExpPhiErr', 'rExpRa', 'rExpRaErr', 'rExpRadius', 'rExpRadiusErr', 'raMean', 'raMeanErr', 'raStack', 'raStackErr', 'randomID', 'rippDetectID', 'rstackDetectID', 'rstackImageID', 'skyCellID', 'surveyID', 'tessID', 'uniquePspsOBid', 'yExpAb', 'yExpAbErr', 'yExpChisq', 'yExpDec', 'yExpDecErr', 'yExpMag', 'yExpMagErr', 'yExpPhi', 'yExpPhiErr', 'yExpRa', 'yExpRaErr', 'yExpRadius', 'yExpRadiusErr', 'yippDetectID', 'ystackDetectID', 'ystackImageID', 'zExpAb', 'zExpAbErr', 'zExpChisq', 'zExpDec', 'zExpDecErr', 'zExpMag', 'zExpMagErr', 'zExpPhi', 'zExpPhiErr', 'zExpRa', 'zExpRaErr', 'zExpRadius', 'zExpRadiusErr', 'zippDetectID', 'zoneID', 'zstackDetectID', 'zstackImageID']
----
dbo.StackModelFitExtra
Contains the galaxy shape and concentration parameters measured from the stack detections. See StackObjectThin table for discussion of primary, secondary, and best detections. References: Blakeslee, J. P., Holden, B. P., Franx, M., et al. 2006, ApJ, 644, 30; Cheng, J. Y., Faber, S. M., Schade, D., Lilly, S. J., Crampton, D., et al. 1995, ApJL, 451, L1; Simard, L., et al. 2011, MNRAS, 412, 727; Simard, L., Willmer, C. N. A., Vogt, N. P., et al. 2005, ApJS, 142, 1.
Columns=['bestDetection', 'gS2', 'gbumpy', 'ghalfLightRad', 'gippDetectID', 'glogRA', 'glogRT', 'gstackDetectID', 'gstackImageID', 'iS2', 'ibumpy', 'ihalfLightRad', 'iippDetectID', 'ilogRA', 'ilogRT', 'ippObjID', 'istackDetectID', 'istackImageID', 'objID', 'primaryDetection', 'rS2', 'randomStackObjID', 'rbumpy', 'rhalfLightRad', 'rippDetectID', 'rlogRA', 'rlogRT', 'rstackDetectID', 'rstackImageID', 'uniquePspsSTid', 'yS2', 'ybumpy', 'yhalfLightRad', 'yippDetectID', 'ylogRA', 'ylogRT', 'ystackDetectID', 'ystackImageID', 'zS2', 'zbumpy', 'zhalfLightRad', 'zippDetectID', 'zlogRA', 'zlogRT', 'zstackDetectID', 'zstackImageID']
----
dbo.StackModelFitPetObjectView
-- ObjectThin join StackPetrosian joined by objID column.
Columns=['b', 'batchID', 'bestDetection', 'beta', 'cx', 'cy', 'cz', 'decMean', 'decMeanErr', 'decStack', 'decStackErr', 'dvoRegionID', 'epochMean', 'gippDetectID', 'gpetCf', 'gpetMag', 'gpetMagErr', 'gpetR50', 'gpetR50Err', 'gpetR90', 'gpetR90Err', 'gpetRadius', 'gpetRadiusErr', 'gstackDetectID', 'gstackImageID', 'htmID', 'iippDetectID', 'ipetCf', 'ipetMag', 'ipetMagErr', 'ipetR50', 'ipetR50Err', 'ipetR90', 'ipetR90Err', 'ipetRadius', 'ipetRadiusErr', 'ippObjID', 'istackDetectID', 'istackImageID', 'l', 'lambda', 'nDetections', 'nStackDetections', 'nStackObjectRows', 'ng', 'ni', 'nr', 'ny', 'nz', 'objAltName1', 'objAltName2', 'objAltName3', 'objID', 'objInfoFlag', 'objName', 'posMeanChisq', 'primaryDetection', 'processingVersion', 'projectionID', 'qualityFlag', 'raMean', 'raMeanErr', 'raStack', 'raStackErr', 'randomID', 'rippDetectID', 'rpetCf', 'rpetMag', 'rpetMagErr', 'rpetR50', 'rpetR50Err', 'rpetR90', 'rpetR90Err', 'rpetRadius', 'rpetRadiusErr', 'rstackDetectID', 'rstackImageID', 'skyCellID', 'surveyID', 'tessID', 'uniquePspsOBid', 'yippDetectID', 'ypetCf', 'ypetMag', 'ypetMagErr', 'ypetR50', 'ypetR50Err', 'ypetR90', 'ypetR90Err', 'ypetRadius', 'ypetRadiusErr', 'ystackDetectID', 'ystackImageID', 'zippDetectID', 'zoneID', 'zpetCf', 'zpetMag', 'zpetMagErr', 'zpetR50', 'zpetR50Err', 'zpetR90', 'zpetR90Err', 'zpetRadius', 'zpetRadiusErr', 'zstackDetectID', 'zstackImageID']
----
dbo.StackModelFitSer
Contains the Sersic (1963) fit parameters to extended sources. See StackObjectThin table for discussion of primary, secondary, and best detections. References: Sersic, J. L. 1963, Boletin de la Asociacion Argentina de Astronomia La Plata Argentina, 6, 41.
Columns=['bestDetection', 'gSerAb', 'gSerAbErr', 'gSerChisq', 'gSerDec', 'gSerDecErr', 'gSerMag', 'gSerMagErr', 'gSerNu', 'gSerNuErr', 'gSerPhi', 'gSerPhiErr', 'gSerRa', 'gSerRaErr', 'gSerRadius', 'gSerRadiusErr', 'gippDetectID', 'gstackDetectID', 'gstackImageID', 'iSerAb', 'iSerAbErr', 'iSerChisq', 'iSerDec', 'iSerDecErr', 'iSerMag', 'iSerMagErr', 'iSerNu', 'iSerNuErr', 'iSerPhi', 'iSerPhiErr', 'iSerRa', 'iSerRaErr', 'iSerRadius', 'iSerRadiusErr', 'iippDetectID', 'ippObjID', 'istackDetectID', 'istackImageID', 'objID', 'primaryDetection', 'rSerAb', 'rSerAbErr', 'rSerChisq', 'rSerDec', 'rSerDecErr', 'rSerMag', 'rSerMagErr', 'rSerNu', 'rSerNuErr', 'rSerPhi', 'rSerPhiErr', 'rSerRa', 'rSerRaErr', 'rSerRadius', 'rSerRadiusErr', 'randomStackObjID', 'rippDetectID', 'rstackDetectID', 'rstackImageID', 'uniquePspsSTid', 'ySerAb', 'ySerAbErr', 'ySerChisq', 'ySerDec', 'ySerDecErr', 'ySerMag', 'ySerMagErr', 'ySerNu', 'ySerNuErr', 'ySerPhi', 'ySerPhiErr', 'ySerRa', 'ySerRaErr', 'ySerRadius', 'ySerRadiusErr', 'yippDetectID', 'ystackDetectID', 'ystackImageID', 'zSerAb', 'zSerAbErr', 'zSerChisq', 'zSerDec', 'zSerDecErr', 'zSerMag', 'zSerMagErr', 'zSerNu', 'zSerNuErr', 'zSerPhi', 'zSerPhiErr', 'zSerRa', 'zSerRaErr', 'zSerRadius', 'zSerRadiusErr', 'zippDetectID', 'zstackDetectID', 'zstackImageID']
----
dbo.StackModelFitSerObjectView
-- ObjectThin join StackModelFitSer joined by objID column.
Columns=['b', 'batchID', 'bestDetection', 'beta', 'cx', 'cy', 'cz', 'decMean', 'decMeanErr', 'decStack', 'decStackErr', 'dvoRegionID', 'epochMean', 'gSerAb', 'gSerAbErr', 'gSerChisq', 'gSerDec', 'gSerDecErr', 'gSerMag', 'gSerMagErr', 'gSerNu', 'gSerNuErr', 'gSerPhi', 'gSerPhiErr', 'gSerRa', 'gSerRaErr', 'gSerRadius', 'gSerRadiusErr', 'gippDetectID', 'gstackDetectID', 'gstackImageID', 'htmID', 'iSerAb', 'iSerAbErr', 'iSerChisq', 'iSerDec', 'iSerDecErr', 'iSerMag', 'iSerMagErr', 'iSerNu', 'iSerNuErr', 'iSerPhi', 'iSerPhiErr', 'iSerRa', 'iSerRaErr', 'iSerRadius', 'iSerRadiusErr', 'iippDetectID', 'ippObjID', 'istackDetectID', 'istackImageID', 'l', 'lambda', 'nDetections', 'nStackDetections', 'nStackObjectRows', 'ng', 'ni', 'nr', 'ny', 'nz', 'objAltName1', 'objAltName2', 'objAltName3', 'objID', 'objInfoFlag', 'objName', 'posMeanChisq', 'primaryDetection', 'processingVersion', 'projectionID', 'qualityFlag', 'rSerAb', 'rSerAbErr', 'rSerChisq', 'rSerDec', 'rSerDecErr', 'rSerMag', 'rSerMagErr', 'rSerNu', 'rSerNuErr', 'rSerPhi', 'rSerPhiErr', 'rSerRa', 'rSerRaErr', 'rSerRadius', 'rSerRadiusErr', 'raMean', 'raMeanErr', 'raStack', 'raStackErr', 'randomID', 'rippDetectID', 'rstackDetectID', 'rstackImageID', 'skyCellID', 'surveyID', 'tessID', 'uniquePspsOBid', 'ySerAb', 'ySerAbErr', 'ySerChisq', 'ySerDec', 'ySerDecErr', 'ySerMag', 'ySerMagErr', 'ySerNu', 'ySerNuErr', 'ySerPhi', 'ySerPhiErr', 'ySerRa', 'ySerRaErr', 'ySerRadius', 'ySerRadiusErr', 'yippDetectID', 'ystackDetectID', 'ystackImageID', 'zSerAb', 'zSerAbErr', 'zSerChisq', 'zSerDec', 'zSerDecErr', 'zSerMag', 'zSerMagErr', 'zSerNu', 'zSerNuErr', 'zSerPhi', 'zSerPhiErr', 'zSerRa', 'zSerRaErr', 'zSerRadius', 'zSerRadiusErr', 'zippDetectID', 'zoneID', 'zstackDetectID', 'zstackImageID']
----
dbo.StackModelObjectView
-- View based on a combination of the tables ObjectThin, StackModelFitExp, StackModelFitDeVm, StackModelFitSer, and StackPetrosian. -- Note: User beware that this view contains a lot of columns and selecting all of them is not recommended.
Columns=['b', 'bestDetection', 'beta', 'cx', 'cy', 'cz', 'decMean', 'decMeanErr', 'decStack', 'decStackErr', 'dvoRegionID', 'gDeVAb', 'gDeVAbErr', 'gDeVChisq', 'gDeVDec', 'gDeVDecErr', 'gDeVMag', 'gDeVMagErr', 'gDeVPhi', 'gDeVPhiErr', 'gDeVRa', 'gDeVRaErr', 'gDeVRadius', 'gDeVRadiusErr', 'gExpAb', 'gExpAbErr', 'gExpChisq', 'gExpDec', 'gExpDecErr', 'gExpMag', 'gExpMagErr', 'gExpPhi', 'gExpPhiErr', 'gExpRa', 'gExpRaErr', 'gExpRadius', 'gExpRadiusErr', 'gSerAb', 'gSerAbErr', 'gSerChisq', 'gSerDec', 'gSerDecErr', 'gSerMag', 'gSerMagErr', 'gSerNu', 'gSerNuErr', 'gSerPhi', 'gSerPhiErr', 'gSerRa', 'gSerRaErr', 'gSerRadius', 'gSerRadiusErr', 'gippDetectID', 'gpetCf', 'gpetMag', 'gpetMagErr', 'gpetR50', 'gpetR50Err', 'gpetR90', 'gpetR90Err', 'gpetRadius', 'gpetRadiusErr', 'gstackDetectID', 'gstackImageID', 'htmID', 'iDeVAb', 'iDeVAbErr', 'iDeVChisq', 'iDeVDec', 'iDeVDecErr', 'iDeVMag', 'iDeVMagErr', 'iDeVPhi', 'iDeVPhiErr', 'iDeVRa', 'iDeVRaErr', 'iDeVRadius', 'iDeVRadiusErr', 'iExpAb', 'iExpAbErr', 'iExpChisq', 'iExpDec', 'iExpDecErr', 'iExpMag', 'iExpMagErr', 'iExpPhi', 'iExpPhiErr', 'iExpRa', 'iExpRaErr', 'iExpRadius', 'iExpRadiusErr', 'iSerAb', 'iSerAbErr', 'iSerChisq', 'iSerDec', 'iSerDecErr', 'iSerMag', 'iSerMagErr', 'iSerNu', 'iSerNuErr', 'iSerPhi', 'iSerPhiErr', 'iSerRa', 'iSerRaErr', 'iSerRadius', 'iSerRadiusErr', 'iippDetectID', 'ipetCf', 'ipetMag', 'ipetMagErr', 'ipetR50', 'ipetR50Err', 'ipetR90', 'ipetR90Err', 'ipetRadius', 'ipetRadiusErr', 'ippObjID', 'istackDetectID', 'istackImageID', 'l', 'lambda', 'nDetections', 'nStackDetections', 'nStackObjectRows', 'ng', 'ni', 'nr', 'ny', 'nz', 'objID', 'objInfoFlag', 'objName', 'posMeanChisq', 'primaryDetection', 'processingVersion', 'projectionID', 'qualityFlag', 'rDeVAb', 'rDeVAbErr', 'rDeVChisq', 'rDeVDec', 'rDeVDecErr', 'rDeVMag', 'rDeVMagErr', 'rDeVPhi', 'rDeVPhiErr', 'rDeVRa', 'rDeVRaErr', 'rDeVRadius', 'rDeVRadiusErr', 'rExpAb', 'rExpAbErr', 'rExpChisq', 'rExpDec', 'rExpDecErr', 'rExpMag', 'rExpMagErr', 'rExpPhi', 'rExpPhiErr', 'rExpRa', 'rExpRaErr', 'rExpRadius', 'rExpRadiusErr', 'rSerAb', 'rSerAbErr', 'rSerChisq', 'rSerDec', 'rSerDecErr', 'rSerMag', 'rSerMagErr', 'rSerNu', 'rSerNuErr', 'rSerPhi', 'rSerPhiErr', 'rSerRa', 'rSerRaErr', 'rSerRadius', 'rSerRadiusErr', 'raMean', 'raMeanErr', 'raStack', 'raStackErr', 'randomID', 'randomStackObjID', 'rippDetectID', 'rpetCf', 'rpetMag', 'rpetMagErr', 'rpetR50', 'rpetR50Err', 'rpetR90', 'rpetR90Err', 'rpetRadius', 'rpetRadiusErr', 'rstackDetectID', 'rstackImageID', 'skyCellID', 'surveyID', 'tessID', 'uniquePspsOBid', 'uniquePspsSTid', 'yDeVAb', 'yDeVAbErr', 'yDeVChisq', 'yDeVDec', 'yDeVDecErr', 'yDeVMag', 'yDeVMagErr', 'yDeVPhi', 'yDeVPhiErr', 'yDeVRa', 'yDeVRaErr', 'yDeVRadius', 'yDeVRadiusErr', 'yExpAb', 'yExpAbErr', 'yExpChisq', 'yExpDec', 'yExpDecErr', 'yExpMag', 'yExpMagErr', 'yExpPhi', 'yExpPhiErr', 'yExpRa', 'yExpRaErr', 'yExpRadius', 'yExpRadiusErr', 'ySerAb', 'ySerAbErr', 'ySerChisq', 'ySerDec', 'ySerDecErr', 'ySerMag', 'ySerMagErr', 'ySerNu', 'ySerNuErr', 'ySerPhi', 'ySerPhiErr', 'ySerRa', 'ySerRaErr', 'ySerRadius', 'ySerRadiusErr', 'yippDetectID', 'ypetCf', 'ypetMag', 'ypetMagErr', 'ypetR50', 'ypetR50Err', 'ypetR90', 'ypetR90Err', 'ypetRadius', 'ypetRadiusErr', 'ystackDetectID', 'ystackImageID', 'zDeVAb', 'zDeVAbErr', 'zDeVChisq', 'zDeVDec', 'zDeVDecErr', 'zDeVMag', 'zDeVMagErr', 'zDeVPhi', 'zDeVPhiErr', 'zDeVRa', 'zDeVRaErr', 'zDeVRadius', 'zDeVRadiusErr', 'zExpAb', 'zExpAbErr', 'zExpChisq', 'zExpDec', 'zExpDecErr', 'zExpMag', 'zExpMagErr', 'zExpPhi', 'zExpPhiErr', 'zExpRa', 'zExpRaErr', 'zExpRadius', 'zExpRadiusErr', 'zSerAb', 'zSerAbErr', 'zSerChisq', 'zSerDec', 'zSerDecErr', 'zSerMag', 'zSerMagErr', 'zSerNu', 'zSerNuErr', 'zSerPhi', 'zSerPhiErr', 'zSerRa', 'zSerRaErr', 'zSerRadius', 'zSerRadiusErr', 'zippDetectID', 'zoneID', 'zpetCf', 'zpetMag', 'zpetMagErr', 'zpetR50', 'zpetR50Err', 'zpetR90', 'zpetR90Err', 'zpetRadius', 'zpetRadiusErr', 'zstackDetectID', 'zstackImageID']
----
dbo.StackObjectAttributes
Contains the PSF, Kron (1980), and aperture fluxes for all filters in a single row, along with point-source object shape parameters. See StackObjectThin table for discussion of primary, secondary, and best detections. References: Kron, R. G. 1980, ApJS, 43, 305.
Columns=['bestDetection', 'gApFillFac', 'gApFlux', 'gApFluxErr', 'gApRadius', 'gExtNSigma', 'gKronFlux', 'gKronFluxErr', 'gKronRad', 'gPSFFlux', 'gPSFFluxErr', 'gPlateScale', 'gexpTime', 'gippDetectID', 'gmomentR1', 'gmomentRH', 'gmomentXX', 'gmomentXY', 'gmomentYY', 'gpsfChiSq', 'gpsfCore', 'gpsfLikelihood', 'gpsfMajorFWHM', 'gpsfMinorFWHM', 'gpsfQf', 'gpsfQfPerfect', 'gpsfTheta', 'gsky', 'gskyErr', 'gstackDetectID', 'gstackImageID', 'gxPos', 'gxPosErr', 'gyPos', 'gyPosErr', 'gzp', 'iApFillFac', 'iApFlux', 'iApFluxErr', 'iApRadius', 'iExtNSigma', 'iKronFlux', 'iKronFluxErr', 'iKronRad', 'iPSFFlux', 'iPSFFluxErr', 'iPlateScale', 'iexpTime', 'iippDetectID', 'imomentR1', 'imomentRH', 'imomentXX', 'imomentXY', 'imomentYY', 'ippObjID', 'ipsfChiSq', 'ipsfCore', 'ipsfLikelihood', 'ipsfMajorFWHM', 'ipsfMinorFWHM', 'ipsfQf', 'ipsfQfPerfect', 'ipsfTheta', 'isky', 'iskyErr', 'istackDetectID', 'istackImageID', 'ixPos', 'ixPosErr', 'iyPos', 'iyPosErr', 'izp', 'objID', 'primaryDetection', 'rApFillFac', 'rApFlux', 'rApFluxErr', 'rApRadius', 'rExtNSigma', 'rKronFlux', 'rKronFluxErr', 'rKronRad', 'rPSFFlux', 'rPSFFluxErr', 'rPlateScale', 'randomStackObjID', 'rexpTime', 'rippDetectID', 'rmomentR1', 'rmomentRH', 'rmomentXX', 'rmomentXY', 'rmomentYY', 'rpsfChiSq', 'rpsfCore', 'rpsfLikelihood', 'rpsfMajorFWHM', 'rpsfMinorFWHM', 'rpsfQf', 'rpsfQfPerfect', 'rpsfTheta', 'rsky', 'rskyErr', 'rstackDetectID', 'rstackImageID', 'rxPos', 'rxPosErr', 'ryPos', 'ryPosErr', 'rzp', 'uniquePspsSTid', 'yApFillFac', 'yApFlux', 'yApFluxErr', 'yApRadius', 'yExtNSigma', 'yKronFlux', 'yKronFluxErr', 'yKronRad', 'yPSFFlux', 'yPSFFluxErr', 'yPlateScale', 'yexpTime', 'yippDetectID', 'ymomentR1', 'ymomentRH', 'ymomentXX', 'ymomentXY', 'ymomentYY', 'ypsfChiSq', 'ypsfCore', 'ypsfLikelihood', 'ypsfMajorFWHM', 'ypsfMinorFWHM', 'ypsfQf', 'ypsfQfPerfect', 'ypsfTheta', 'ysky', 'yskyErr', 'ystackDetectID', 'ystackImageID', 'yxPos', 'yxPosErr', 'yyPos', 'yyPosErr', 'yzp', 'zApFillFac', 'zApFlux', 'zApFluxErr', 'zApRadius', 'zExtNSigma', 'zKronFlux', 'zKronFluxErr', 'zKronRad', 'zPSFFlux', 'zPSFFluxErr', 'zPlateScale', 'zexpTime', 'zippDetectID', 'zmomentR1', 'zmomentRH', 'zmomentXX', 'zmomentXY', 'zmomentYY', 'zpsfChiSq', 'zpsfCore', 'zpsfLikelihood', 'zpsfMajorFWHM', 'zpsfMinorFWHM', 'zpsfQf', 'zpsfQfPerfect', 'zpsfTheta', 'zsky', 'zskyErr', 'zstackDetectID', 'zstackImageID', 'zxPos', 'zxPosErr', 'zyPos', 'zyPosErr', 'zzp']
----
dbo.StackObjectThin
Contains the positional and photometric information for point-source photometry of stack detections. The information for all filters are joined into a single row, with metadata indicating if this stack object represents the primary detection. Due to overlaps in the stack tessellations, an object may appear in multiple stack images. The primary detection is the unique detection from the stack image that provides the best coverage with minimal projection stretching. All other detections of the object in that filter are secondary, regardless of their properties. The detection flagged as best is the primary detection if that detection has a psfQf value greater than 0.98; if that is not met, then any of the primary or secondary detections with the highest psfQf value is flagged as best. References: Kron, R. G. 1980, ApJS, 43, 305; Magnier et al. 2015, in prep.
Columns=['bestDetection', 'dvoRegionID', 'gApMag', 'gApMagErr', 'gEpoch', 'gKronMag', 'gKronMagErr', 'gPSFMag', 'gPSFMagErr', 'gdec', 'gdecErr', 'ginfoFlag', 'ginfoFlag2', 'ginfoFlag3', 'ginfoFlag4', 'gippDetectID', 'gnFrames', 'gra', 'graErr', 'gstackDetectID', 'gstackImageID', 'iApMag', 'iApMagErr', 'iEpoch', 'iKronMag', 'iKronMagErr', 'iPSFMag', 'iPSFMagErr', 'idec', 'idecErr', 'iinfoFlag', 'iinfoFlag2', 'iinfoFlag3', 'iinfoFlag4', 'iippDetectID', 'inFrames', 'ippObjID', 'ira', 'iraErr', 'istackDetectID', 'istackImageID', 'objID', 'primaryDetection', 'processingVersion', 'projectionID', 'rApMag', 'rApMagErr', 'rEpoch', 'rKronMag', 'rKronMagErr', 'rPSFMag', 'rPSFMagErr', 'randomStackObjID', 'rdec', 'rdecErr', 'rinfoFlag', 'rinfoFlag2', 'rinfoFlag3', 'rinfoFlag4', 'rippDetectID', 'rnFrames', 'rra', 'rraErr', 'rstackDetectID', 'rstackImageID', 'skyCellID', 'surveyID', 'tessID', 'uniquePspsSTid', 'yApMag', 'yApMagErr', 'yEpoch', 'yKronMag', 'yKronMagErr', 'yPSFMag', 'yPSFMagErr', 'ydec', 'ydecErr', 'yinfoFlag', 'yinfoFlag2', 'yinfoFlag3', 'yinfoFlag4', 'yippDetectID', 'ynFrames', 'yra', 'yraErr', 'ystackDetectID', 'ystackImageID', 'zApMag', 'zApMagErr', 'zEpoch', 'zKronMag', 'zKronMagErr', 'zPSFMag', 'zPSFMagErr', 'zdec', 'zdecErr', 'zinfoFlag', 'zinfoFlag2', 'zinfoFlag3', 'zinfoFlag4', 'zippDetectID', 'znFrames', 'zra', 'zraErr', 'zstackDetectID', 'zstackImageID']
----
dbo.StackObjectView
-- View based on a combination of the tables ObjectThin, StackObjectThin and StackObjectAttributes joined by objID column.
Columns=['b', 'batchID', 'bestDetection', 'beta', 'cx', 'cy', 'cz', 'decMean', 'decMeanErr', 'decStack', 'decStackErr', 'dvoRegionID', 'epochMean', 'gApFillFac', 'gApFlux', 'gApFluxErr', 'gApMag', 'gApMagErr', 'gApRadius', 'gEpoch', 'gExtNSigma', 'gKronFlux', 'gKronFluxErr', 'gKronMag', 'gKronMagErr', 'gKronRad', 'gPSFFlux', 'gPSFFluxErr', 'gPSFMag', 'gPSFMagErr', 'gPlateScale', 'gdec', 'gdecErr', 'gexpTime', 'ginfoFlag', 'ginfoFlag2', 'ginfoFlag3', 'gippDetectID', 'gmomentR1', 'gmomentRH', 'gmomentXX', 'gmomentXY', 'gmomentYY', 'gnFrames', 'gpsfChiSq', 'gpsfCore', 'gpsfLikelihood', 'gpsfMajorFWHM', 'gpsfMinorFWHM', 'gpsfQf', 'gpsfQfPerfect', 'gpsfTheta', 'gra', 'graErr', 'gsky', 'gskyErr', 'gstackDetectID', 'gstackImageID', 'gxPos', 'gxPosErr', 'gyPos', 'gyPosErr', 'gzp', 'htmID', 'iApFillFac', 'iApFlux', 'iApFluxErr', 'iApMag', 'iApMagErr', 'iApRadius', 'iEpoch', 'iExtNSigma', 'iKronFlux', 'iKronFluxErr', 'iKronMag', 'iKronMagErr', 'iKronRad', 'iPSFFlux', 'iPSFFluxErr', 'iPSFMag', 'iPSFMagErr', 'iPlateScale', 'idec', 'idecErr', 'iexpTime', 'iinfoFlag', 'iinfoFlag2', 'iinfoFlag3', 'iippDetectID', 'imomentR1', 'imomentRH', 'imomentXX', 'imomentXY', 'imomentYY', 'inFrames', 'ippObjID', 'ipsfChiSq', 'ipsfCore', 'ipsfLikelihood', 'ipsfMajorFWHM', 'ipsfMinorFWHM', 'ipsfQf', 'ipsfQfPerfect', 'ipsfTheta', 'ira', 'iraErr', 'isky', 'iskyErr', 'istackDetectID', 'istackImageID', 'ixPos', 'ixPosErr', 'iyPos', 'iyPosErr', 'izp', 'l', 'lambda', 'nDetections', 'nStackDetections', 'nStackObjectRows', 'ng', 'ni', 'nr', 'ny', 'nz', 'objAltName1', 'objAltName2', 'objAltName3', 'objID', 'objInfoFlag', 'objName', 'posMeanChisq', 'primaryDetection', 'processingVersion', 'projectionID', 'qualityFlag', 'rApFillFac', 'rApFlux', 'rApFluxErr', 'rApMag', 'rApMagErr', 'rApRadius', 'rEpoch', 'rExtNSigma', 'rKronFlux', 'rKronFluxErr', 'rKronMag', 'rKronMagErr', 'rKronRad', 'rPSFFlux', 'rPSFFluxErr', 'rPSFMag', 'rPSFMagErr', 'rPlateScale', 'raMean', 'raMeanErr', 'raStack', 'raStackErr', 'randomID', 'rdec', 'rdecErr', 'rexpTime', 'rinfoFlag', 'rinfoFlag2', 'rinfoFlag3', 'rippDetectID', 'rmomentR1', 'rmomentRH', 'rmomentXX', 'rmomentXY', 'rmomentYY', 'rnFrames', 'rpsfChiSq', 'rpsfCore', 'rpsfLikelihood', 'rpsfMajorFWHM', 'rpsfMinorFWHM', 'rpsfQf', 'rpsfQfPerfect', 'rpsfTheta', 'rra', 'rraErr', 'rsky', 'rskyErr', 'rstackDetectID', 'rstackImageID', 'rxPos', 'rxPosErr', 'ryPos', 'ryPosErr', 'rzp', 'skyCellID', 'surveyID', 'tessID', 'uniquePspsOBid', 'uniquePspsSTid', 'yApFillFac', 'yApFlux', 'yApFluxErr', 'yApMag', 'yApMagErr', 'yApRadius', 'yEpoch', 'yExtNSigma', 'yKronFlux', 'yKronFluxErr', 'yKronMag', 'yKronMagErr', 'yKronRad', 'yPSFFlux', 'yPSFFluxErr', 'yPSFMag', 'yPSFMagErr', 'yPlateScale', 'ydec', 'ydecErr', 'yexpTime', 'yinfoFlag', 'yinfoFlag2', 'yinfoFlag3', 'yippDetectID', 'ymomentR1', 'ymomentRH', 'ymomentXX', 'ymomentXY', 'ymomentYY', 'ynFrames', 'ypsfChiSq', 'ypsfCore', 'ypsfLikelihood', 'ypsfMajorFWHM', 'ypsfMinorFWHM', 'ypsfQf', 'ypsfQfPerfect', 'ypsfTheta', 'yra', 'yraErr', 'ysky', 'yskyErr', 'ystackDetectID', 'ystackImageID', 'yxPos', 'yxPosErr', 'yyPos', 'yyPosErr', 'yzp', 'zApFillFac', 'zApFlux', 'zApFluxErr', 'zApMag', 'zApMagErr', 'zApRadius', 'zEpoch', 'zExtNSigma', 'zKronFlux', 'zKronFluxErr', 'zKronMag', 'zKronMagErr', 'zKronRad', 'zPSFFlux', 'zPSFFluxErr', 'zPSFMag', 'zPSFMagErr', 'zPlateScale', 'zdec', 'zdecErr', 'zexpTime', 'zinfoFlag', 'zinfoFlag2', 'zinfoFlag3', 'zippDetectID', 'zmomentR1', 'zmomentRH', 'zmomentXX', 'zmomentXY', 'zmomentYY', 'znFrames', 'zoneID', 'zpsfChiSq', 'zpsfCore', 'zpsfLikelihood', 'zpsfMajorFWHM', 'zpsfMinorFWHM', 'zpsfQf', 'zpsfQfPerfect', 'zpsfTheta', 'zra', 'zraErr', 'zsky', 'zskyErr', 'zstackDetectID', 'zstackImageID', 'zxPos', 'zxPosErr', 'zyPos', 'zyPosErr', 'zzp']
----
dbo.StackPetrosian
Contains the Petrosian (1976) magnitudes and radii for extended sources. See StackObjectThin table for discussion of primary, secondary, and best detections. References: Petrosian, V. 1976, ApJL, 209, L1.
Columns=['bestDetection', 'gippDetectID', 'gpetCf', 'gpetMag', 'gpetMagErr', 'gpetR50', 'gpetR50Err', 'gpetR90', 'gpetR90Err', 'gpetRadius', 'gpetRadiusErr', 'gstackDetectID', 'gstackImageID', 'iippDetectID', 'ipetCf', 'ipetMag', 'ipetMagErr', 'ipetR50', 'ipetR50Err', 'ipetR90', 'ipetR90Err', 'ipetRadius', 'ipetRadiusErr', 'ippObjID', 'istackDetectID', 'istackImageID', 'objID', 'primaryDetection', 'randomStackObjID', 'rippDetectID', 'rpetCf', 'rpetMag', 'rpetMagErr', 'rpetR50', 'rpetR50Err', 'rpetR90', 'rpetR90Err', 'rpetRadius', 'rpetRadiusErr', 'rstackDetectID', 'rstackImageID', 'uniquePspsSTid', 'yippDetectID', 'ypetCf', 'ypetMag', 'ypetMagErr', 'ypetR50', 'ypetR50Err', 'ypetR90', 'ypetR90Err', 'ypetRadius', 'ypetRadiusErr', 'ystackDetectID', 'ystackImageID', 'zippDetectID', 'zpetCf', 'zpetMag', 'zpetMagErr', 'zpetR50', 'zpetR50Err', 'zpetR90', 'zpetR90Err', 'zpetRadius', 'zpetRadiusErr', 'zstackDetectID', 'zstackImageID']
----
dbo.StackToFrame
Contains the mapping of input frames used to construct a particular stack along with processing stats.
Columns=['airMass', 'expTime', 'frameID', 'scaleFactor', 'stackImageID', 'zp']
----
dbo.StackToImage
Contains the mapping of which input images were used to construct a particular stack.
Columns=['imageID', 'stackImageID']
----
dbo.StackType
Contains descriptions of the types of stacked images constructed.
Columns=['description', 'name', 'stackTypeID']
----
dbo.Survey
Contains descriptions of the various PS1 Science Consortium Surveys.
Columns=['description', 'name', 'surveyID']
----
dbo.TessellationType
Contains descriptions of the types of image tessellations for the sky.
Columns=['description', 'name', 'tessID']
----
Use Cases#
Simple Positional Query#
This searches the mean object catalog for objects within .2 degrees of M87 (RA=187.706, Dec=12.391 in degrees). The view used contains information from the ObjectThin table (which has information on object positions and the number of available measurements) and the MeanObject table (which has information on photometry averaged over the multiple epochs of observation).
Note that the results are restricted to objects with nDetections>1, where nDetections is the total number of times the object was detected on the single-epoch images in any filter at any time. Objects with nDetections=1 tend to be artifacts, so this is a quick way to eliminate most spurious objects from the catalog.
This query runs in TAP’s asynchronous mode, which is a queued batch mode with some overhead and longer timeouts, useful for big catalogs like PanSTARRS. It may not be necessary for all queries to PS1 DR2, but the PyVO client can automatically handle the additional processing required over synchronous mode.
job = TAP_service.run_async("""
SELECT objID, RAMean, DecMean, nDetections, ng, nr, ni, nz, ny, gMeanPSFMag, rMeanPSFMag, iMeanPSFMag, zMeanPSFMag, yMeanPSFMag
FROM dbo.MeanObjectView
WHERE
CONTAINS(POINT('ICRS', RAMean, DecMean),CIRCLE('ICRS',187.706,12.391,.2))=1
AND nDetections > 1
""")
TAP_results = job.to_table()
TAP_results
Table length=6927
| objid | ramean | decmean | ndetections | ng | nr | ni | nz | ny | gmeanpsfmag | rmeanpsfmag | imeanpsfmag | zmeanpsfmag | ymeanpsfmag |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| deg | deg | mag | mag | mag | mag | mag | |||||||
| int64 | float64 | float64 | int16 | int16 | int16 | int16 | int16 | int16 | float32 | float32 | float32 | float32 | float32 |
| 122861877059169881 | 187.70591003309602 | 12.39113345997396 | 54 | 10 | 10 | 14 | 8 | 12 | 10.295 | 10.732 | 11.076 | 11.342 | 11.557 |
| 122861877056308967 | 187.70559948 | 12.39030211 | 2 | 0 | 0 | 0 | 0 | 2 | -999.0 | -999.0 | -999.0 | -999.0 | 14.9985 |
| 122871877063310741 | 187.70631118732143 | 12.39181079630862 | 3 | 0 | 2 | 0 | 1 | 0 | -999.0 | 14.4229 | -999.0 | 14.4249 | -999.0 |
| 122861877058698594 | 187.70580919 | 12.39013311 | 2 | 0 | 0 | 0 | 0 | 2 | -999.0 | -999.0 | -999.0 | -999.0 | 14.7305 |
| 122861877050678994 | 187.70512542608552 | 12.390430557059906 | 5 | 2 | 2 | 1 | 0 | 0 | 17.0287 | 15.0843 | 16.169 | -999.0 | -999.0 |
| 122871877062551436 | 187.70622365 | 12.39236505 | 2 | 0 | 0 | 0 | 0 | 2 | -999.0 | -999.0 | -999.0 | -999.0 | 15.1102 |
| 122861877044629638 | 187.70451598 | 12.39086724 | 2 | 0 | 0 | 0 | 2 | 0 | -999.0 | -999.0 | -999.0 | 15.5294 | -999.0 |
| 122861877056688054 | 187.70558996 | 12.38960002 | 2 | 0 | 0 | 0 | 2 | 0 | -999.0 | -999.0 | -999.0 | 14.4064 | -999.0 |
| 122871877075050430 | 187.70747912 | 12.3915839 | 2 | 0 | 0 | 0 | 2 | 0 | -999.0 | -999.0 | -999.0 | 15.5752 | -999.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 123101877313707983 | 187.73121143 | 12.58941058 | 2 | 0 | 0 | 0 | 2 | 0 | -999.0 | -999.0 | -999.0 | 19.9209 | -999.0 |
| 122901875031002013 | 187.50314518107095 | 12.417915440212205 | 37 | 7 | 10 | 11 | 7 | 2 | 21.2326 | 20.7473 | 20.5742 | 20.6803 | 20.2113 |
| 122661878055210091 | 187.80550736264715 | 12.216281397817458 | 57 | 13 | 14 | 12 | 6 | 12 | 20.9059 | 20.3516 | 20.0699 | 19.9956 | 19.8391 |
| 122631877536376320 | 187.75369336 | 12.19655761 | 2 | 0 | 0 | 0 | 2 | 0 | -999.0 | -999.0 | -999.0 | 21.0599 | -999.0 |
| 123031875562633406 | 187.55622393040358 | 12.527354381090412 | 3 | 0 | 0 | 3 | 0 | 0 | -999.0 | -999.0 | 20.5518 | -999.0 | -999.0 |
| 122681878343382931 | 187.83438155722584 | 12.235251674424532 | 24 | 5 | 9 | 6 | 4 | 0 | 22.0912 | 21.7042 | 21.3068 | 21.0164 | -999.0 |
| 123061878259604285 | 187.82588872 | 12.55313763 | 2 | 1 | 0 | 1 | 0 | 0 | 21.9049 | -999.0 | 21.4564 | -999.0 | -999.0 |
| 122751878844752179 | 187.88446793636015 | 12.293022278414929 | 77 | 16 | 24 | 14 | 11 | 12 | 21.4623 | 20.3555 | 20.1394 | 20.0368 | 19.8008 |
| 123001875376856622 | 187.53785086789503 | 12.505173082359276 | 4 | 0 | 3 | 1 | 0 | 0 | -999.0 | 21.1887 | 21.1419 | -999.0 | -999.0 |
| 123101876640214622 | 187.66398717765725 | 12.586746063373354 | 3 | 0 | 0 | 2 | 1 | 0 | -999.0 | -999.0 | 21.8531 | 21.6726 | -999.0 |
Get DR2 light curve for RR Lyrae star KQ UMa#
This time we start with the object name, use the MAST name resolver (which relies on Simbad and NED) to convert the name to RA and Dec, and then query the PS1 DR2 mean object catalog at that position. Then we run a spatial query to TAP using those coordinates.
objname = 'KQ UMa'
coords = Mast.resolve_object(objname)
ra,dec = coords.ra.value,coords.dec.value
radius = 1.0/3600.0 # radius = 1 arcsec
query = """
SELECT objID, RAMean, DecMean, nDetections, ng, nr, ni, nz, ny, gMeanPSFMag,
rMeanPSFMag, iMeanPSFMag, zMeanPSFMag, yMeanPSFMag
FROM dbo.MeanObjectView
WHERE
CONTAINS(POINT('ICRS', RAMean, DecMean),CIRCLE('ICRS',{},{},{}))=1
AND nDetections > 1
""".format(ra,dec,radius)
print(query)
job = TAP_service.run_async(query)
TAP_results = job.to_table()
TAP_results
SELECT objID, RAMean, DecMean, nDetections, ng, nr, ni, nz, ny, gMeanPSFMag,
rMeanPSFMag, iMeanPSFMag, zMeanPSFMag, yMeanPSFMag
FROM dbo.MeanObjectView
WHERE
CONTAINS(POINT('ICRS', RAMean, DecMean),CIRCLE('ICRS',139.33446271609,68.63508880829,0.0002777777777777778))=1
AND nDetections > 1
Table length=1
| objid | ramean | decmean | ndetections | ng | nr | ni | nz | ny | gmeanpsfmag | rmeanpsfmag | imeanpsfmag | zmeanpsfmag | ymeanpsfmag |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| deg | deg | mag | mag | mag | mag | mag | |||||||
| int64 | float64 | float64 | int16 | int16 | int16 | int16 | int16 | int16 | float32 | float32 | float32 | float32 | float32 |
| 190361393344112894 | 139.33445305334158 | 68.63505916169231 | 66 | 8 | 10 | 21 | 13 | 14 | 15.0402 | 14.553 | 14.2109 | 14.2814 | 14.3041 |
Get Repeated Detection Information#
Extract all the objects with the same object ID from the Detection table, which contains all the individual measurements for this source. The results are joined to the Filter table to convert the filter numbers to names.
objid = TAP_results['objid'][0]
query = """
SELECT
objID, detectID, Detection.filterID as filterID, Filter.filterType, obsTime, ra, dec,
psfFlux, psfFluxErr, psfMajorFWHM, psfMinorFWHM, psfQfPerfect,
apFlux, apFluxErr, infoFlag, infoFlag2, infoFlag3
FROM Detection
NATURAL JOIN Filter
WHERE objID={}
ORDER BY filterID, obsTime
""".format(objid)
print(query)
job = TAP_service.run_async(query)
detection_TAP_results = job.to_table()
detection_TAP_results
SELECT
objID, detectID, Detection.filterID as filterID, Filter.filterType, obsTime, ra, dec,
psfFlux, psfFluxErr, psfMajorFWHM, psfMinorFWHM, psfQfPerfect,
apFlux, apFluxErr, infoFlag, infoFlag2, infoFlag3
FROM Detection
NATURAL JOIN Filter
WHERE objID=190361393344112894
ORDER BY filterID, obsTime
Table length=66
| objID | detectID | filterid | filterType | obsTime | ra | dec | psfFlux | psfFluxErr | psfMajorFWHM | psfMinorFWHM | psfQfPerfect | apFlux | apFluxErr | infoFlag | infoFlag2 | infoFlag3 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| deg | deg | |||||||||||||||
| int64 | int64 | uint8 | object | float64 | float64 | float64 | float32 | float32 | float32 | float32 | float32 | float32 | float32 | int64 | int32 | int32 |
| 190361393344112894 | 153347716310000010 | 1 | g | 55634.477414 | 139.3345207 | 68.63503577 | 0.00826192 | 1.14074e-05 | 1.88185 | 1.76734 | 0.992916 | 0.00861705 | 1.14233e-05 | 102760517 | 128 | 124782656 |
| 190361393344112894 | 153348968310000008 | 1 | g | 55634.4899457 | 139.33448821 | 68.63506146 | 0.0077373 | 1.10268e-05 | 1.81031 | 1.60518 | 0.998461 | 0.00792172 | 1.09406e-05 | 102760517 | 128 | 124782656 |
| 190361393344112894 | 232228791560000017 | 1 | g | 56423.2881719 | 139.33449441 | 68.63503952 | 0.00335198 | 7.26745e-06 | 1.60204 | 1.45048 | 0.998589 | 0.00338658 | 7.2363e-06 | 102760517 | 128 | 108038208 |
| 190361393344112894 | 255559866370000015 | 1 | g | 56656.5989211 | 139.33444592 | 68.63504589 | 0.00372909 | 7.37688e-06 | 1.82831 | 1.68692 | 0.999191 | 0.00372163 | 7.52088e-06 | 102760517 | 128 | 124815424 |
| 190361393344112894 | 262040070370000016 | 1 | g | 56721.4009635 | 139.33445148 | 68.63504807 | 0.003576 | 7.22894e-06 | 1.60585 | 1.50396 | 0.998605 | 0.00359603 | 7.43463e-06 | 102760517 | 128 | 7374912 |
| 190361393344112894 | 262040708370000014 | 1 | g | 56721.4073411 | 139.33445304 | 68.63504406 | 0.00350554 | 7.14809e-06 | 1.62415 | 1.48827 | 0.999487 | 0.00359624 | 7.43434e-06 | 102760517 | 128 | 7374912 |
| 190361393344112894 | 264231864260000022 | 1 | g | 56743.3189045 | 139.3344431 | 68.63504497 | 0.00343867 | 7.81574e-06 | 1.53957 | 1.4761 | 0.999049 | 0.00361239 | 7.85237e-06 | 102760517 | 128 | 7374912 |
| 190361393344112894 | 264232516260000024 | 1 | g | 56743.3254198 | 139.33444512 | 68.63505285 | 0.00347403 | 7.87789e-06 | 1.52751 | 1.43325 | 0.999367 | 0.00360349 | 7.8686e-06 | 102760517 | 128 | 7374912 |
| 190361393344112894 | 153441340410000012 | 2 | r | 55635.4136426 | 139.33447173 | 68.6350534 | 0.00978758 | 1.10843e-05 | 1.67879 | 1.5653 | 0.998509 | 0.00990763 | 1.11231e-05 | 102760517 | 128 | 124815424 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 190361393344112894 | 183254354520000014 | 5 | y | 55933.5437238 | 139.3344534 | 68.63505904 | 0.0079394 | 2.4924e-05 | 0.959231 | 0.901458 | 0.996853 | 0.00803078 | 2.37126e-05 | 102760517 | 128 | 7374912 |
| 190361393344112894 | 183255163520000015 | 5 | y | 55933.5518128 | 139.33444232 | 68.63505669 | 0.00915573 | 2.64422e-05 | 1.11331 | 0.95368 | 0.997115 | 0.00914515 | 2.48501e-05 | 102760517 | 128 | 74483776 |
| 190361393344112894 | 191726725200000011 | 5 | y | 56018.2674345 | 139.33446079 | 68.63506403 | 0.00829646 | 2.59461e-05 | 1.07332 | 1.01563 | 0.997924 | 0.00833759 | 2.33303e-05 | 102760517 | 128 | 7374912 |
| 190361393344112894 | 191727369200000012 | 5 | y | 56018.2738664 | 139.33445189 | 68.63506152 | 0.00819996 | 2.55524e-05 | 1.09504 | 1.05914 | 0.997819 | 0.00820753 | 2.3153e-05 | 102760517 | 128 | 7374912 |
| 190361393344112894 | 229122569150000014 | 5 | y | 56392.2258719 | 139.33442742 | 68.63506234 | 0.00780422 | 2.57689e-05 | 1.02878 | 0.893195 | 0.998618 | 0.00777279 | 2.16455e-05 | 102760517 | 128 | 7374912 |
| 190361393344112894 | 229123273150000011 | 5 | y | 56392.2329084 | 139.33444595 | 68.63505744 | 0.00803503 | 2.55974e-05 | 0.941801 | 0.888788 | 0.99805 | 0.00807037 | 2.2092e-05 | 102760517 | 128 | 7374912 |
| 190361393344112894 | 264322648150000024 | 5 | y | 56744.2269452 | 139.334454 | 68.63505864 | 0.00540446 | 1.32146e-05 | 1.38829 | 1.3477 | 0.998922 | 0.00549867 | 1.13527e-05 | 102760517 | 128 | 124782656 |
| 190361393344112894 | 265623008150000027 | 5 | y | 56757.2305455 | 139.33444319 | 68.63506375 | 0.00636614 | 1.60784e-05 | 0.895041 | 0.803365 | 0.852516 | 0.00412859 | 1.01303e-05 | 102760581 | 128 | 34880 |
| 190361393344112894 | 287165374630000023 | 5 | y | 56972.6539243 | 139.33444226 | 68.63506087 | 0.00687817 | 2.79326e-05 | 1.53686 | 1.28813 | 0.991647 | 0.00699914 | 2.16585e-05 | 102760517 | 128 | 7374912 |
| 190361393344112894 | 287265116630000028 | 5 | y | 56973.6513438 | 139.33444991 | 68.63505759 | 0.00665377 | 2.7704e-05 | 1.85619 | 1.70397 | 0.99864 | 0.00663106 | 2.13156e-05 | 102760517 | 128 | 7342144 |
Plot the light curves#
The psfFlux values from the Detection table are converted from Janskys to AB magnitudes. Measurements in the 5 different filters are plotted separately.
# convert flux in Jy to magnitudes
t = detection_TAP_results['obsTime']
mag = -2.5*np.log10(detection_TAP_results['psfFlux']) + 8.90
xlim = np.array([t.min(),t.max()])
xlim = xlim + np.array([-1,1])*0.02*(xlim[1]-xlim[0])
pylab.rcParams.update({'font.size': 14})
pylab.figure(1,(10,10))
#detection_TAP_results['filterType'] is a byte string, compare accordingly:
for i, filter in enumerate(['g','r','i','z','y']):
pylab.subplot(511+i)
w = np.where(detection_TAP_results['filterType'] == filter)
pylab.plot(t[w],mag[w],'-o')
pylab.ylabel(f'{filter} [mag]')
pylab.xlim(xlim)
pylab.gca().invert_yaxis()
if i==0:
pylab.title(objname)
pylab.xlabel('Time [MJD]')
pylab.tight_layout()
Plot differences from the mean magnitudes in the initial search.
# convert flux in Jy to magnitudes
t = detection_TAP_results['obsTime']
mag = -2.5*np.log10(detection_TAP_results['psfFlux']) + 8.90
xlim = np.array([t.min(),t.max()])
xlim = xlim + np.array([-1,1])*0.02*(xlim[1]-xlim[0])
pylab.rcParams.update({'font.size': 14})
pylab.figure(1,(10,10))
#detection_TAP_results['filterType'] is a byte string, compare accordingly:
for i, filter in enumerate(['g','r','i','z','y']):
pylab.subplot(511+i)
w = np.where(detection_TAP_results['filterType']==filter)
magmean = TAP_results[f'{filter}meanpsfmag'][0]
pylab.plot(t[w],mag[w] - magmean,'-o')
pylab.ylabel(f'{filter} [mag - {np.round(float(magmean), 4)}]')
pylab.xlim(xlim)
pylab.gca().invert_yaxis()
if i==0:
pylab.title(objname)
pylab.xlabel('Time [MJD]')
pylab.tight_layout()
Identify bad data#
There is one clearly bad $z$ magnitude with a very large difference. Select the bad point and look at it in more detail.
Note that indexing a table (or numpy array) with a logical expression selects just the rows where that expression is true.
detection_TAP_results[ (detection_TAP_results['filterType']=='z') & (np.abs(mag-TAP_results['zmeanpsfmag'][0]) > 2) ]
Table length=1
| objID | detectID | filterid | filterType | obsTime | ra | dec | psfFlux | psfFluxErr | psfMajorFWHM | psfMinorFWHM | psfQfPerfect | apFlux | apFluxErr | infoFlag | infoFlag2 | infoFlag3 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| deg | deg | |||||||||||||||
| int64 | int64 | uint8 | object | float64 | float64 | float64 | float32 | float32 | float32 | float32 | float32 | float32 | float32 | int64 | int32 | int32 |
| 190361393344112894 | 183252627520000234 | 4 | z | 55933.5264577 | 139.33488168 | 68.63532273 | 0.000317945 | 6.73008e-06 | 1.07537 | 1.0153 | 0.322986 | 0.000213217 | 2.36939e-06 | 102760453 | 128 | 32768 |
From examining this table, it looks like psfQfPerfect is bad. This flag is the PSF-weighted fraction of unmasked pixels in the image (see the documentation for more details). Values near unity indicate good data that is not significantly affected by bad pixels.
Check all the psfQfPerfect values for the $z$ filter to see if this value really is unusual. The list of values below are sorted by magnitude. The bad point is the only value with psfQfPerfect < 0.95.
w = np.where(detection_TAP_results['filterType']=='z')
zdtab = detection_TAP_results[w]
zdtab['mag'] = mag[w]
zdtab['dmag'] = zdtab['mag'] - TAP_results['zmeanpsfmag'][0]
ii = np.argsort(-np.abs(zdtab['dmag']))
zdtab = zdtab[ii]
zdtab['objID','obsTime','mag','dmag','psfQfPerfect']
Table length=13
| objID | obsTime | mag | dmag | psfQfPerfect |
|---|---|---|---|---|
| int64 | float64 | float32 | float32 | float32 |
| 190361393344112894 | 55933.5264577 | 17.64412 | 3.3627195 | 0.322986 |
| 190361393344112894 | 56289.6159346 | 13.890659 | -0.3907404 | 0.997811 |
| 190361393344112894 | 56289.6241112 | 13.916806 | -0.3645935 | 0.988369 |
| 190361393344112894 | 56351.4168483 | 13.998973 | -0.28242683 | 0.999257 |
| 190361393344112894 | 55281.2528285 | 14.537914 | 0.25651455 | 0.99754 |
| 190361393344112894 | 56351.424076 | 14.032502 | -0.24889755 | 0.999187 |
| 190361393344112894 | 55527.6508919 | 14.512117 | 0.23071766 | 0.997265 |
| 190361393344112894 | 56648.5676019 | 14.05643 | -0.22496986 | 0.997982 |
| 190361393344112894 | 55527.6381469 | 14.465141 | 0.18374157 | 0.99738 |
| 190361393344112894 | 55281.2625151 | 14.428555 | 0.14715481 | 0.955584 |
| 190361393344112894 | 55933.534515 | 14.30764 | 0.026240349 | 0.997489 |
| 190361393344112894 | 56019.2968782 | 14.278636 | -0.0027637482 | 0.997654 |
| 190361393344112894 | 56019.3038014 | 14.282891 | 0.0014915466 | 0.997338 |
Repeat the plot with bad psfQfPerfect values excluded#
Do the plot again but exclude low psfQfPerfect values.
# convert flux in Jy to magnitudes
t = detection_TAP_results['obsTime']
mag = -2.5*np.log10(detection_TAP_results['psfFlux']) + 8.90
magmean = 0.0*mag
for i, filter in enumerate(['g','r','i','z','y']):
magmean[detection_TAP_results['filterType']==filter] = TAP_results[f'{filter}meanpsfmag'][0]
dmag = mag - magmean
dmag1 = dmag[detection_TAP_results['psfQfPerfect']>0.9]
# fix the x and y axis ranges
xlim = np.array([t.min(),t.max()])
xlim = xlim + np.array([-1,1])*0.02*(xlim[1]-xlim[0])
# flip axis direction for magnitude
ylim = np.array([dmag1.max(),dmag1.min()])
ylim = ylim + np.array([-1,1])*0.02*(ylim[1]-ylim[0])
pylab.rcParams.update({'font.size': 14})
pylab.figure(1,(10,10))
for i, filter in enumerate(['g','r','i','z','y']):
pylab.subplot(511+i)
w = np.where((detection_TAP_results['filterType']==filter) & (detection_TAP_results['psfQfPerfect']>0.9))[0]
pylab.plot(t[w],dmag[w],'-o')
pylab.ylabel('{} [mag - {:.2f}]'.format(filter,magmean[w[0]]))
pylab.xlim(xlim)
pylab.ylim(ylim)
if i==0:
pylab.title(objname)
pylab.xlabel('Time [MJD]')
pylab.tight_layout()
Plot versus the periodic phase instead of epoch#
Plot versus phase using known RR Lyr period from Simbad (table J/AJ/132/1202/table4).
period = 0.48636 #days, from Simbad
# convert flux in Jy to magnitudes
t = (detection_TAP_results['obsTime'] % period) / period
mag = -2.5*np.log10(detection_TAP_results['psfFlux']) + 8.90
magmean = 0.0*mag
for i, filter in enumerate(['g','r','i','z','y']):
magmean[detection_TAP_results['filterType']==filter] = TAP_results[f'{filter}meanpsfmag'][0]
dmag = mag - magmean
dmag1 = dmag[detection_TAP_results['psfQfPerfect']>0.9]
# fix the x and y axis ranges
xlim = np.array([t.min(),t.max()])
xlim = xlim + np.array([-1,1])*0.02*(xlim[1]-xlim[0])
# flip axis direction for magnitude
ylim = np.array([dmag1.max(),dmag1.min()])
ylim = ylim + np.array([-1,1])*0.02*(ylim[1]-ylim[0])
pylab.rcParams.update({'font.size': 14})
pylab.figure(1,(10,10))
for i, filter in enumerate(['g','r','i','z','y']):
pylab.subplot(511+i)
w = np.where((detection_TAP_results['filterType']==filter) & (detection_TAP_results['psfQfPerfect']>0.9))[0]
w = w[np.argsort(t[w])]
pylab.plot(t[w],dmag[w],'-o')
pylab.ylabel('{} [mag - {:.2f}]'.format(filter,magmean[w[0]]))
pylab.xlim(xlim)
pylab.ylim(ylim)
if i==0:
pylab.title(objname)
pylab.xlabel('Phase')
pylab.tight_layout()
Repeat search using eclipsing binary KIC 2161623#
From Villanova Kepler Eclipsing Binaries
objname = 'KIC 2161623'
coords = Mast.resolve_object(objname)
ra,dec = coords.ra.value,coords.dec.value
radius = 1.0/3600.0 # radius = 1 arcsec
query = """
SELECT objID, RAMean, DecMean, nDetections, ng, nr, ni, nz, ny, gMeanPSFMag, rMeanPSFMag, iMeanPSFMag, zMeanPSFMag, yMeanPSFMag
FROM dbo.MeanObjectView
WHERE
CONTAINS(POINT('ICRS', RAMean, DecMean),CIRCLE('ICRS',{},{},{}))=1
AND nDetections > 1
""".format(ra,dec,radius)
print(query)
job = TAP_service.run_async(query)
TAP_results = job.to_table()
TAP_results
SELECT objID, RAMean, DecMean, nDetections, ng, nr, ni, nz, ny, gMeanPSFMag, rMeanPSFMag, iMeanPSFMag, zMeanPSFMag, yMeanPSFMag
FROM dbo.MeanObjectView
WHERE
CONTAINS(POINT('ICRS', RAMean, DecMean),CIRCLE('ICRS',291.744461,37.59102,0.0002777777777777778))=1
AND nDetections > 1
Table length=1
| objid | ramean | decmean | ndetections | ng | nr | ni | nz | ny | gmeanpsfmag | rmeanpsfmag | imeanpsfmag | zmeanpsfmag | ymeanpsfmag |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| deg | deg | mag | mag | mag | mag | mag | |||||||
| int64 | float64 | float64 | int16 | int16 | int16 | int16 | int16 | int16 | float32 | float32 | float32 | float32 | float32 |
| 153102917444859851 | 291.74446283634614 | 37.59099888154969 | 67 | 10 | 16 | 12 | 15 | 14 | 14.5998 | 14.2821 | 14.1587 | 14.2004 | 14.0672 |
Get Repeated Detection Information#
This time include the psfQfPerfect limit directly in the database query.
objid = TAP_results['objid'][0]
query = """
SELECT
objID, detectID, Detection.filterID as filterID, Filter.filterType, obsTime, ra, dec,
psfFlux, psfFluxErr, psfMajorFWHM, psfMinorFWHM, psfQfPerfect,
apFlux, apFluxErr, infoFlag, infoFlag2, infoFlag3
FROM Detection
NATURAL JOIN Filter
WHERE objID={}
AND psfQfPerfect >= 0.9
ORDER BY filterID, obsTime
""".format(objid)
print(query)
job = TAP_service.run_async(query)
detection_TAP_results = job.to_table()
# add magnitude and difference from mean
detection_TAP_results['magmean'] = 0.0
for i, filter in enumerate([b'g',b'r',b'i',b'z',b'y']):
detection_TAP_results['magmean'][detection_TAP_results['filterType']==filter] = TAP_results[filter.decode('ascii')+'meanpsfmag'][0]
detection_TAP_results['mag'] = -2.5*np.log10(detection_TAP_results['psfFlux']) + 8.90
detection_TAP_results['dmag'] = detection_TAP_results['mag']-detection_TAP_results['magmean']
detection_TAP_results
SELECT
objID, detectID, Detection.filterID as filterID, Filter.filterType, obsTime, ra, dec,
psfFlux, psfFluxErr, psfMajorFWHM, psfMinorFWHM, psfQfPerfect,
apFlux, apFluxErr, infoFlag, infoFlag2, infoFlag3
FROM Detection
NATURAL JOIN Filter
WHERE objID=153102917444859851
AND psfQfPerfect >= 0.9
ORDER BY filterID, obsTime
Table length=45
| objID | detectID | filterid | filterType | obsTime | ra | dec | psfFlux | psfFluxErr | psfMajorFWHM | psfMinorFWHM | psfQfPerfect | apFlux | apFluxErr | infoFlag | infoFlag2 | infoFlag3 | magmean | mag | dmag |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| deg | deg | ||||||||||||||||||
| int64 | int64 | uint8 | object | float64 | float64 | float64 | float32 | float32 | float32 | float32 | float32 | float32 | float32 | int64 | int32 | int32 | float64 | float32 | float64 |
| 153102917444859851 | 90150443710000088 | 1 | g | 55002.5047803 | 291.74446391 | 37.5910006 | 0.00501008 | 6.65687e-06 | 1.98744 | 1.81211 | 0.999421 | 0.00517413 | 7.31225e-06 | 102760517 | 128 | 124815424 | 0.0 | 14.650389 | 14.650388717651367 |
| 153102917444859851 | 90151959710000087 | 1 | g | 55002.5199475 | 291.74446611 | 37.59100156 | 0.00487334 | 6.48844e-06 | 1.98487 | 1.70793 | 0.999234 | 0.00511542 | 7.27714e-06 | 102760517 | 128 | 124815424 | 0.0 | 14.680433 | 14.68043327331543 |
| 153102917444859851 | 131534996560000129 | 1 | g | 55416.3502179 | 291.74446082 | 37.59099692 | 0.00525622 | 8.41646e-06 | 1.62403 | 1.57893 | 0.998566 | 0.0052992 | 8.62578e-06 | 102760517 | 128 | 7374912 | 0.0 | 14.598316 | 14.598316192626953 |
| 153102917444859851 | 131536180650000138 | 1 | g | 55416.362065 | 291.74445937 | 37.59099733 | 0.00533028 | 8.62169e-06 | 1.32081 | 1.27186 | 0.998757 | 0.00540082 | 8.91577e-06 | 102760517 | 128 | 124815424 | 0.0 | 14.583125 | 14.583125114440918 |
| 153102917444859851 | 204528930560000076 | 1 | g | 56146.2895546 | 291.74445914 | 37.59098073 | 0.00522399 | 8.43794e-06 | 1.33369 | 1.29211 | 0.999758 | 0.00532506 | 8.88465e-06 | 102760517 | 128 | 7374912 | 0.0 | 14.604994 | 14.60499382019043 |
| 153102917444859851 | 204530041560000085 | 1 | g | 56146.3006673 | 291.74446486 | 37.59097649 | 0.00523539 | 8.44281e-06 | 1.42883 | 1.22683 | 0.964646 | 0.00532419 | 8.85594e-06 | 102760517 | 128 | 7374912 | 0.0 | 14.602628 | 14.602627754211426 |
| 153102917444859851 | 241032265120000094 | 1 | g | 56511.322909 | 291.74445657 | 37.59099752 | 0.00528641 | 8.3427e-06 | 1.15123 | 1.10789 | 0.998722 | 0.00537484 | 8.8135e-06 | 102760517 | 128 | 7374912 | 0.0 | 14.592098 | 14.592098236083984 |
| 153102917444859851 | 241033321120000081 | 1 | g | 56511.3334693 | 291.74445945 | 37.59099643 | 0.00527889 | 8.31263e-06 | 1.07898 | 0.987323 | 0.995279 | 0.0053346 | 8.77771e-06 | 102760517 | 128 | 7374912 | 0.0 | 14.593643 | 14.593643188476562 |
| 153102917444859851 | 91252543710000102 | 2 | r | 55013.5256677 | 291.74445985 | 37.59099454 | 0.00700243 | 8.7848e-06 | 1.90187 | 1.68689 | 0.998962 | 0.00723513 | 9.82915e-06 | 102760517 | 128 | 7374912 | 0.0 | 14.286878 | 14.286877632141113 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 153102917444859851 | 159961616410000125 | 5 | y | 55700.6163536 | 291.74446346 | 37.59099768 | 0.00867286 | 2.66106e-05 | 0.780035 | 0.650466 | 0.959502 | 0.00873106 | 2.5663e-05 | 102760517 | 128 | 7374912 | 0.0 | 14.054594 | 14.054594039916992 |
| 153102917444859851 | 160460087410000136 | 5 | y | 55705.6010605 | 291.74446372 | 37.59099667 | 0.00838914 | 2.4518e-05 | 0.892294 | 0.785714 | 0.997498 | 0.00850705 | 2.38093e-05 | 102760517 | 128 | 7374912 | 0.0 | 14.090706 | 14.090705871582031 |
| 153102917444859851 | 160461274410000134 | 5 | y | 55705.6129264 | 291.74446479 | 37.59099484 | 0.00846376 | 2.45964e-05 | 0.843567 | 0.719354 | 0.982199 | 0.00845989 | 2.36938e-05 | 102760517 | 128 | 7374912 | 0.0 | 14.081092 | 14.08109188079834 |
| 153102917444859851 | 171734372010000127 | 5 | y | 55818.3439014 | 291.74446419 | 37.59099973 | 0.00899537 | 2.60801e-05 | 1.25605 | 0.963624 | 0.997509 | 0.00893125 | 2.37426e-05 | 102760517 | 128 | 57706560 | 0.0 | 14.014953 | 14.014952659606934 |
| 153102917444859851 | 171735523010000129 | 5 | y | 55818.3554064 | 291.74446058 | 37.59100208 | 0.00893885 | 2.61524e-05 | 1.2684 | 0.941259 | 0.997929 | 0.00898301 | 2.38145e-05 | 102760517 | 128 | 40929344 | 0.0 | 14.021795 | 14.021795272827148 |
| 153102917444859851 | 194462075640000160 | 5 | y | 56045.6209324 | 291.7444627 | 37.59099914 | 0.00901312 | 2.46438e-05 | 1.41157 | 1.29344 | 0.998375 | 0.00900578 | 2.28883e-05 | 102760517 | 128 | 57706560 | 0.0 | 14.012812 | 14.012811660766602 |
| 153102917444859851 | 194462752640000158 | 5 | y | 56045.6277083 | 291.74445989 | 37.59099718 | 0.00881642 | 2.44413e-05 | 1.40377 | 1.19898 | 0.999512 | 0.00888709 | 2.26963e-05 | 102760517 | 128 | 7374912 | 0.0 | 14.03677 | 14.03676986694336 |
| 153102917444859851 | 213319324300000159 | 5 | y | 56234.1934155 | 291.74446373 | 37.5910009 | 0.00851151 | 2.42822e-05 | 1.04504 | 0.849606 | 0.99497 | 0.00842352 | 2.41261e-05 | 102760517 | 128 | 7374912 | 0.0 | 14.074984 | 14.074983596801758 |
| 153102917444859851 | 213319880300000163 | 5 | y | 56234.1989869 | 291.74446915 | 37.59100079 | 0.00852216 | 2.36733e-05 | 1.03871 | 0.843969 | 0.994977 | 0.00833225 | 2.42534e-05 | 102760517 | 128 | 74483776 | 0.0 | 14.073626 | 14.073625564575195 |
| 153102917444859851 | 245335559360000173 | 5 | y | 56554.3560604 | 291.74446488 | 37.59099874 | 0.00870705 | 1.611e-05 | 1.42906 | 1.38935 | 0.959646 | 0.00886884 | 1.39697e-05 | 102760517 | 128 | 40929344 | 0.0 | 14.050323 | 14.050322532653809 |
t = detection_TAP_results['obsTime']
dmag = detection_TAP_results['dmag']
xlim = np.array([t.min(),t.max()])
xlim = xlim + np.array([-1,1])*0.02*(xlim[1]-xlim[0])
ylim = np.array([dmag.max(),dmag.min()])
ylim = ylim + np.array([-1,1])*0.02*(ylim[1]-ylim[0])
pylab.rcParams.update({'font.size': 14})
pylab.figure(1,(10,10))
for i, filter in enumerate(['g','r','i','z','y']):
pylab.subplot(511+i)
w = np.where(detection_TAP_results['filterType']==filter)[0]
pylab.plot(t[w],dmag[w],'-o')
magmean = detection_TAP_results['magmean'][w[0]]
pylab.ylabel('{} [mag - {:.2f}]'.format(filter,magmean))
pylab.xlim(xlim)
pylab.ylim(ylim)
if i==0:
pylab.title(objname)
pylab.xlabel('Time [MJD]')
pylab.tight_layout()
Plot versus phase using known period#
Eclipsing binaries basically vary by same amount in all filters since it is a geometrical effect, so combine the data into a single light curve. Wrap using known period and plot versus phase.
period = 2.2834698
bjd0 = 54999.599837
t = ((detection_TAP_results['obsTime']-bjd0) % period) / period
dmag = detection_TAP_results['dmag']
w = np.argsort(t)
t = t[w]
dmag = dmag[w]
xlim = np.array([t.min(),t.max()])
xlim = xlim + np.array([-1,1])*0.02*(xlim[1]-xlim[0])
ylim = np.array([dmag.max(),dmag.min()])
ylim = ylim + np.array([-1,1])*0.02*(ylim[1]-ylim[0])
pylab.rcParams.update({'font.size': 14})
pylab.figure(1,(10,6))
pylab.plot(t,dmag,'-o')
pylab.xlim(xlim)
pylab.ylim(ylim)
pylab.xlabel('Phase')
pylab.ylabel('Delta magnitude from mean [mag]')
pylab.title(objname)
pylab.tight_layout()
Repeat search for another eclipsing binary KIC 8153568#
objname = 'KIC 8153568'
coords = Mast.resolve_object(objname)
ra,dec = coords.ra.value,coords.dec.value
radius = 1.0/3600.0 # radius = 1 arcsec
query = """
SELECT objID, RAMean, DecMean, nDetections, ng, nr, ni, nz, ny, gMeanPSFMag, rMeanPSFMag, iMeanPSFMag, zMeanPSFMag, yMeanPSFMag
FROM dbo.MeanObjectView
WHERE
CONTAINS(POINT('ICRS', RAMean, DecMean),CIRCLE('ICRS',{},{},{}))=1
AND nDetections > 1
""".format(ra,dec,radius)
print(query)
job = TAP_service.run_async(query)
TAP_results = job.to_table()
TAP_results
SELECT objID, RAMean, DecMean, nDetections, ng, nr, ni, nz, ny, gMeanPSFMag, rMeanPSFMag, iMeanPSFMag, zMeanPSFMag, yMeanPSFMag
FROM dbo.MeanObjectView
WHERE
CONTAINS(POINT('ICRS', RAMean, DecMean),CIRCLE('ICRS',286.90445,44.00551,0.0002777777777777778))=1
AND nDetections > 1
Table length=1
| objid | ramean | decmean | ndetections | ng | nr | ni | nz | ny | gmeanpsfmag | rmeanpsfmag | imeanpsfmag | zmeanpsfmag | ymeanpsfmag |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| deg | deg | mag | mag | mag | mag | mag | |||||||
| int64 | float64 | float64 | int16 | int16 | int16 | int16 | int16 | int16 | float32 | float32 | float32 | float32 | float32 |
| 160802869044447231 | 286.90445005122217 | 44.00547945471509 | 88 | 16 | 15 | 31 | 10 | 16 | 15.1825 | 14.9899 | 14.8907 | 15.1999 | 14.8484 |
objid = TAP_results['objid'][0]
query = """
SELECT
objID, detectID, Detection.filterID as filterID, Filter.filterType, obsTime, ra, dec,
psfFlux, psfFluxErr, psfMajorFWHM, psfMinorFWHM, psfQfPerfect,
apFlux, apFluxErr, infoFlag, infoFlag2, infoFlag3
FROM Detection
NATURAL JOIN Filter
WHERE objID={}
AND psfQfPerfect >= 0.9
ORDER BY filterID, obsTime
""".format(objid)
print(query)
job = TAP_service.run_async(query)
detection_TAP_results = job.to_table()
# add magnitude and difference from mean
detection_TAP_results['magmean'] = 0.0
for i, filter in enumerate([b'g',b'r',b'i',b'z',b'y']):
detection_TAP_results['magmean'][detection_TAP_results['filterType']==filter] = TAP_results[filter.decode('ascii')+'meanpsfmag'][0]
detection_TAP_results['mag'] = -2.5*np.log10(detection_TAP_results['psfFlux']) + 8.90
detection_TAP_results['dmag'] = detection_TAP_results['mag']-detection_TAP_results['magmean']
detection_TAP_results
SELECT
objID, detectID, Detection.filterID as filterID, Filter.filterType, obsTime, ra, dec,
psfFlux, psfFluxErr, psfMajorFWHM, psfMinorFWHM, psfQfPerfect,
apFlux, apFluxErr, infoFlag, infoFlag2, infoFlag3
FROM Detection
NATURAL JOIN Filter
WHERE objID=160802869044447231
AND psfQfPerfect >= 0.9
ORDER BY filterID, obsTime
Table length=60
| objID | detectID | filterid | filterType | obsTime | ra | dec | psfFlux | psfFluxErr | psfMajorFWHM | psfMinorFWHM | psfQfPerfect | apFlux | apFluxErr | infoFlag | infoFlag2 | infoFlag3 | magmean | mag | dmag |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| deg | deg | ||||||||||||||||||
| int64 | int64 | uint8 | object | float64 | float64 | float64 | float32 | float32 | float32 | float32 | float32 | float32 | float32 | int64 | int32 | int32 | float64 | float32 | float64 |
| 160802869044447231 | 91336429430000113 | 1 | g | 55014.3646518 | 286.90443374 | 44.00547575 | 0.003109 | 5.98065e-06 | 1.78 | 1.23811 | 0.998335 | 0.00312447 | 5.69291e-06 | 102760517 | 128 | 7374912 | 0.0 | 15.1684475 | 15.168447494506836 |
| 160802869044447231 | 91337480430000116 | 1 | g | 55014.3751566 | 286.90443894 | 44.00547502 | 0.00313056 | 6.00144e-06 | 1.66939 | 1.32773 | 0.998197 | 0.00315613 | 5.71358e-06 | 102760517 | 128 | 124815424 | 0.0 | 15.160945 | 15.160944938659668 |
| 160802869044447231 | 126057565120000092 | 1 | g | 55361.57592 | 286.90445317 | 44.00547451 | 0.00309573 | 6.72312e-06 | 1.7498 | 1.62422 | 0.998826 | 0.00310045 | 6.69387e-06 | 102760517 | 128 | 7374912 | 0.0 | 15.173092 | 15.173091888427734 |
| 160802869044447231 | 126058741130000111 | 1 | g | 55361.587678 | 286.90445157 | 44.00548065 | 0.00302815 | 6.57752e-06 | 1.62839 | 1.52579 | 0.998387 | 0.0031062 | 6.76425e-06 | 102760517 | 128 | 7374912 | 0.0 | 15.197056 | 15.19705581665039 |
| 160802869044447231 | 128744126310000284 | 1 | g | 55388.4415151 | 286.9044502 | 44.00547891 | 0.000806462 | 3.51444e-06 | 1.10132 | 0.992501 | 0.997631 | 0.000810416 | 3.45294e-06 | 102760517 | 128 | 124815424 | 0.0 | 16.633541 | 16.633541107177734 |
| 160802869044447231 | 128745341310000249 | 1 | g | 55388.4536712 | 286.90445043 | 44.00547936 | 0.000981762 | 3.82737e-06 | 1.1085 | 1.07441 | 0.997491 | 0.000985584 | 3.78859e-06 | 102760517 | 128 | 124815424 | 0.0 | 16.419985 | 16.419984817504883 |
| 160802869044447231 | 164544841720000097 | 1 | g | 55746.4486739 | 286.90445315 | 44.00547646 | 0.00306655 | 6.32653e-06 | 1.32851 | 1.16693 | 0.99831 | 0.00304733 | 6.63894e-06 | 102760517 | 128 | 40929344 | 0.0 | 15.183374 | 15.183374404907227 |
| 160802869044447231 | 164744408530000111 | 1 | g | 55748.4443394 | 286.90445207 | 44.00547835 | 0.00304057 | 6.5461e-06 | 1.37153 | 1.23193 | 0.998544 | 0.00307828 | 6.69725e-06 | 102760517 | 128 | 7374912 | 0.0 | 15.192612 | 15.192611694335938 |
| 160802869044447231 | 164745526530000113 | 1 | g | 55748.4555151 | 286.90445275 | 44.0054797 | 0.00304728 | 6.57104e-06 | 1.27531 | 1.26424 | 0.998519 | 0.00308564 | 6.71944e-06 | 102760517 | 128 | 7374912 | 0.0 | 15.190219 | 15.190218925476074 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 160802869044447231 | 281422935550000135 | 4 | z | 56915.2295351 | 286.9044441 | 44.00547162 | 0.00405251 | 1.10757e-05 | 1.31143 | 1.13385 | 0.999588 | 0.00405212 | 1.03719e-05 | 102760517 | 128 | 124815424 | 0.0 | 14.88069 | 14.88068962097168 |
| 160802869044447231 | 136027009120000145 | 5 | y | 55461.2702758 | 286.90445503 | 44.00547827 | 0.00429769 | 1.96242e-05 | 1.48652 | 1.38968 | 0.998686 | 0.00423395 | 1.58905e-05 | 102760517 | 128 | 7374912 | 0.0 | 14.816912 | 14.816911697387695 |
| 160802869044447231 | 136028204120000148 | 5 | y | 55461.2822322 | 286.90445994 | 44.00548229 | 0.00417461 | 2.0278e-05 | 1.76506 | 1.713 | 0.998705 | 0.00419736 | 1.56164e-05 | 102760517 | 128 | 7374912 | 0.0 | 14.84846 | 14.84846019744873 |
| 160802869044447231 | 159959993400000147 | 5 | y | 55700.6001108 | 286.90444692 | 44.00548086 | 0.00400983 | 1.95314e-05 | 1.04048 | 0.898651 | 0.998029 | 0.00396177 | 1.71112e-05 | 102760517 | 128 | 40929344 | 0.0 | 14.892185 | 14.89218521118164 |
| 160802869044447231 | 159961165400000142 | 5 | y | 55700.611833 | 286.90444463 | 44.00548094 | 0.00409667 | 1.92559e-05 | 0.876026 | 0.741052 | 0.998806 | 0.00403949 | 1.75641e-05 | 102760517 | 128 | 7374912 | 0.0 | 14.868923 | 14.86892318725586 |
| 160802869044447231 | 160459626400000159 | 5 | y | 55705.5964454 | 286.9044489 | 44.0054811 | 0.00331343 | 1.64053e-05 | 1.0118 | 0.84848 | 0.997465 | 0.00327993 | 1.48499e-05 | 102760517 | 128 | 124815424 | 0.0 | 15.099305 | 15.099305152893066 |
| 160802869044447231 | 160460795400000171 | 5 | y | 55705.6081345 | 286.90444684 | 44.00547964 | 0.00321194 | 1.64645e-05 | 1.08121 | 0.990937 | 0.997873 | 0.0031801 | 1.44718e-05 | 102760517 | 128 | 124815424 | 0.0 | 15.133081 | 15.133081436157227 |
| 160802869044447231 | 171023586640000136 | 5 | y | 55811.2360427 | 286.9044525 | 44.00548357 | 0.0042144 | 1.82169e-05 | 0.723151 | 0.662475 | 0.9978 | 0.00425246 | 1.73593e-05 | 102760517 | 128 | 7342144 | 0.0 | 14.8381605 | 14.838160514831543 |
| 160802869044447231 | 171024776640000151 | 5 | y | 55811.2479472 | 286.90445276 | 44.00548227 | 0.00425027 | 1.862e-05 | 0.763658 | 0.692114 | 0.997703 | 0.00431336 | 1.74107e-05 | 102760517 | 128 | 7342144 | 0.0 | 14.8289585 | 14.828958511352539 |
| 160802869044447231 | 245335002470000153 | 5 | y | 56554.3504901 | 286.90445085 | 44.00547784 | 0.00427093 | 1.24367e-05 | 1.46348 | 1.40394 | 0.972607 | 0.00426433 | 9.76066e-06 | 102760517 | 128 | 7374912 | 0.0 | 14.823694 | 14.823694229125977 |
t = detection_TAP_results['obsTime']
dmag = detection_TAP_results['dmag']
xlim = np.array([t.min(),t.max()])
xlim = xlim + np.array([-1,1])*0.02*(xlim[1]-xlim[0])
ylim = np.array([dmag.max(),dmag.min()])
ylim = ylim + np.array([-1,1])*0.02*(ylim[1]-ylim[0])
pylab.rcParams.update({'font.size': 14})
pylab.figure(1,(10,10))
for i, filter in enumerate(['g','r','i','z','y']):
pylab.subplot(511+i)
w = np.where(detection_TAP_results['filterType']==filter)[0]
pylab.plot(t[w],dmag[w],'-o')
magmean = detection_TAP_results['magmean'][w[0]]
pylab.ylabel('{} [mag - {:.2f}]'.format(filter,magmean))
pylab.xlim(xlim)
pylab.ylim(ylim)
if i==0:
pylab.title(objname)
pylab.xlabel('Time [MJD]')
pylab.tight_layout()
Eclipsing binaries basically vary by same amount in all filters since it is a geometrical effect, so combine the data into a single light curve.
Wrap using known period and plot versus phase. Plot two periods of the light curve this time.
This nice light curve appears to show a secondary eclipse.
period = 3.6071431
bjd0 = 54999.289794
t = ((detection_TAP_results['obsTime']-bjd0) % period) / period
dmag = detection_TAP_results['dmag']
w = np.argsort(t)
# extend to two periods
nw = len(w)
w = np.append(w,w)
t = t[w]
# add one to second period
t[-nw:] += 1
dmag = dmag[w]
xlim = [0,2.0]
ylim = np.array([dmag.max(),dmag.min()])
ylim = ylim + np.array([-1,1])*0.02*(ylim[1]-ylim[0])
pylab.rcParams.update({'font.size': 14})
pylab.figure(1,(12,6))
pylab.plot(t,dmag,'-o')
pylab.xlim(xlim)
pylab.ylim(ylim)
pylab.xlabel('Phase')
pylab.ylabel('Delta magnitude from mean [mag]')
pylab.title(objname)
pylab.tight_layout()
Additional Resources#
Table Access Protocol#
IVOA standard for RESTful web service access to tabular data
http://www.ivoa.net/documents/TAP/
PanSTARRS 1 DR 2#
Catalog for PanSTARRS with additional Detection information
https://outerspace.stsci.edu/display/PANSTARRS/
Astronomical Query Data Language (2.0)#
IVOA standard for querying astronomical data in tabular format, with geometric search support
http://www.ivoa.net/documents/latest/ADQL.html
PyVO#
an affiliated package for astropy
find and retrieve astronomical data available from archives that support standard IVOA virtual observatory service protocols.
https://pyvo.readthedocs.io/en/latest/index.html
About this Notebook#
Authors: Rick White & Theresa Dower, STScI Archive Scientist & Software Engineer
Last Updated: Jan 2023
For support, please contact the Archive HelpDesk at archive@stsci.edu.
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