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

1. TAP Service Introduction

2. Imports

3. Connecting to a TAP Service

4. Use Cases

5. Additional Resources

6. 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

There are MANY tables available, so we’ll only print out the first few. Use the commented-out for loop to see them all.

TAP_tables = TAP_service.tables

# only show the first three tables
for i, tablename in enumerate(TAP_tables.keys()):
    if i < 10 and not "tap_schema" in tablename:
        TAP_tables[tablename].describe()
        print("Columns={}".format(sorted([k.name for k in TAP_tables[tablename].columns ])))
        print("----")

## PRINT ALL 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.Detection
No description

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
No description

Columns=['"value"', 'description', 'hexadecimal', 'name']
----

dbo.DetectionFlags2
No description

Columns=['"value"', 'description', 'hexadecimal', 'name']
----
dbo.DetectionFlags3
No description

Columns=['"value"', 'description', 'hexadecimal', 'name']
----

dbo.DetectionObjectView
No description

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']
----

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

/opt/hostedtoolcache/Python/3.11.10/x64/lib/python3.11/site-packages/pyvo/dal/query.py:325: DALOverflowWarning: Partial result set. Potential causes MAXREC, async storage space, etc.
  warn("Partial result set. Potential causes MAXREC, async storage space, etc.",

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
...	...	...	...	...	...	...	...	...	...	...	...	...	...
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
				yr	deg	deg			pix	pix
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	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
				yr	deg	deg			pix	pix
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
	yr
int64	float64	float64	float64	float32
190361393344112894	55933.5264577	17.644120001792906	3.3627202749252305	0.322986
190361393344112894	56289.6159346	13.890659356117249	-0.3907403707504269	0.997811
190361393344112894	56289.6241112	13.91680612564087	-0.3645936012268063	0.988369
190361393344112894	56351.4168483	13.998972916603089	-0.28242681026458705	0.999257
190361393344112894	55281.2528285	14.537914538383484	0.25651481151580846	0.99754
190361393344112894	56351.424076	14.032501721382141	-0.2488980054855343	0.999187
190361393344112894	55527.6508919	14.512118244171143	0.23071851730346715	0.997265
190361393344112894	56648.5676019	14.056430006027222	-0.22496972084045375	0.997982
190361393344112894	55527.6381469	14.465140843391419	0.18374111652374303	0.99738
190361393344112894	55281.2625151	14.42855372428894	0.147153997421265	0.955584
190361393344112894	55933.534515	14.307639741897583	0.026240015029907582	0.997489
190361393344112894	56019.2968782	14.278636121749878	-0.0027636051177974963	0.997654
190361393344112894	56019.3038014	14.282891297340393	0.0014915704727176404	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
				yr	deg	deg			pix	pix
int64	int64	uint8	object	float64	float64	float64	float32	float32	float32	float32	float32	float32	float32	int64	int32	int32	float64	float64	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	14.650388503074646	14.650388503074646
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	14.680433416366578	14.680433416366578
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	14.598316097259522	14.598316097259522
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	14.583125257492066	14.583125257492066
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	14.604994201660157	14.604994201660157
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	14.602627301216126	14.602627301216126
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	14.592098140716553	14.592098140716553
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	14.593643689155579	14.593643689155579
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	14.28687825202942	14.28687825202942
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
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	14.090706253051758	14.090706253051758
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	14.081092023849488	14.081092023849488
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	14.014952325820923	14.014952325820923
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	14.021796131134034	14.021796131134034
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	14.012811923027039	14.012811923027039
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	14.03676941394806	14.03676941394806
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	14.074983739852906	14.074983739852906
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	14.073625946044922	14.073625946044922
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	14.050322318077088	14.050322318077088

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

/opt/hostedtoolcache/Python/3.11.10/x64/lib/python3.11/site-packages/pyvo/dal/query.py:325: DALOverflowWarning: Partial result set. Potential causes MAXREC, async storage space, etc.
  warn("Partial result set. Potential causes MAXREC, async storage space, etc.",

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
				yr	deg	deg			pix	pix
int64	int64	uint8	object	float64	float64	float64	float32	float32	float32	float32	float32	float32	float32	int64	int32	int32	float64	float64	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	15.168448233604432	15.168448233604432
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	15.160945200920105	15.160945200920105
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	15.173092031478882	15.173092031478882
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	15.197056674957276	15.197056674957276
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	16.633539938926695	16.633539938926695
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	16.41998424530029	16.41998424530029
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	15.183375024795533	15.183375024795533
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	15.192612552642823	15.192612552642823
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	15.190218830108643	15.190218830108643
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
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	14.81691255569458	14.81691255569458
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	14.8484601020813	14.8484601020813
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	14.89218487739563	14.89218487739563
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	14.86892237663269	14.86892237663269
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	15.099305653572083	15.099305653572083
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	15.133081221580506	15.133081221580506
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	14.838160419464112	14.838160419464112
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	14.828958654403687	14.828958654403687
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	14.823693776130677	14.823693776130677

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()

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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

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About this Notebook

Authors: Rick White & Theresa Dower, STScI Archive Scientist & Software Engineer
Last Updated: Feb 2024

For support, please contact the Archive HelpDesk at archive@stsci.edu.

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