Searching for Mission-Specific Data with Astroquery

Searching for Mission-Specific Data with Astroquery#

Learning Goals#

By the end of this tutorial, you will:

Understand how to use the astroquery.mast module to access mission dataset metadata from MAST.
Run metadata queries based on coordinates, an object name, or non-positional criteria.
Filter and download data products associated with datasets of interest.
Search for datasets from multiple missions and among High Level Science Products (HLSPs).

Table of Contents#

Introduction
Imports
Querying for Datasets from Missions-MAST
- Search Parameters
- Query by Object Name
- Query by Region
- Query by Criteria
Getting Data Products
- Performing a Product Query
- Filtering Data Products
Downloading Products
- Exclusive Data Access
Switching Missions
Exercises
Exercise Solutions
Additional Resources

Introduction#

Welcome! This tutorial explores the capabilities of the astroquery.mast.MastMissions class, a versatile tool for accessing and working with datasets hosted by the Mikulski Archive for Space Telescopes (MAST). MastMissions is a Python wrapper for the MAST Search API, which allows you to search for mission-specific dataset metadata and data products. This data is also findable through the MAST Search UI.

The following missions/products are available for search as of January 2025:

Hubble Space Telescope (hst)
James Webb Space Telescope (jwst)
High Level Science Products
- COS Legacy Archive Spectroscopic SurveY (classy)
- Hubble UV Legacy Library of Young Stars as Essential Standards (ullyses)

In this notebook, we will walk through the basic workflow for searching datasets, retrieving data products, and downloading data products. This workflow will look very similar to the one used with the astroquery.mast.Observations class, detailed in our “Searching MAST using astroquery.mast” notebook. There are a few key differences to note, and you should use the class that is best suited for your unique goals:

API: MastMissions uses the Mast Search API while Observations uses the MAST Portal API.
Collection: MastMissions can only perform queries on a single collection, or “mission”, at a time. Observations uses the Common Archive Observation Model (CAOM) and can run queries across every available observational collection at the same time.
Filter Keywords: MastMissions has an extensive selection of mission-specific keywords to use while writing queries. Observations is limited to the fields described by the CAOM and has no criteria with mission-specific meaning.

In summary, MastMissions is well-suited for fast, mission-specific queries that might require a more extensive selection of filter keywords. Observations is better for more broad, multi-mission searches.

Imports#

This notebook uses the following packages:

astropy to handle astronomical units and coordinate systems
astroquery.mast to query the MAST Archive

import astropy.units as u
from astropy.coordinates import SkyCoord
from astroquery.mast import MastMissions

Querying for Datasets from Missions-MAST#

In order to make queries on Missions-MAST metadata, we will have to perform some setup. We will initialize an object of the astroquery.mast.MastMissions class and assign its mission attribute. The object can be used to search mission dataset metadata by object name, sky position, or other criteria.

The default value for mission is hst, meaning that queries will be run on Hubble dataset metadata. The searchable metadata for Hubble encompasses all information that was previously accessible through the original HST web search form. The metadata for Hubble and all other available missions is also available through the MAST Search UI.

Later in the tutorial, we will learn how to change the mission attribute to make queries on other missions.

# Create MastMissions object to search for Hubble datasets
missions = MastMissions(mission='hst')
missions.mission

'hst'

Search Parameters#

When writing queries, keyword arguments can be used to specify output characteristics and filter on fields like instrument, exposure type, and proposal ID. The available column names for a mission are returned by the get_column_list function. Below, we will print out the name, data type, and description for the first 10 columns in HST metadata.

# Get available columns for HST mission
columns = missions.get_column_list()
columns[:10]

Table length=10

name	data_type	description
str22	str9	str226
search_pos	string	Search Position (RA and Dec)
sci_data_set_name	string	Data set name, the first character indicates instrument; L=COS; I=WFC3; J=ACS; N=NICMOS; O=STIS; U=WFPC2; W=WFPC; X=FOC; Y=FOS; Z=GHRS; F=FGS; V=HSP; nine-character name (e.g. J8BA7JCAQ, O4140Q020)
sci_targname	string	Target name designated by the observer for the HST proposal; Uppercase; No blank characters; Spaces sometimes filled with - ; (e.g. A901-FIELD-25, NGC4486-POS1, 0537-441INCA221-36, ALPHA-CEN)
sci_hapnum	boolean	Reports if there are any Hubble Advanced Products (HAP), enter 0 for no, 1 for yes
sci_haspnum	boolean	Reports if there are any Hubble Advanced Spectral Products (HASP), enter 0 for no, 1 for yes
sci_instrume	string	Instrument used (e.g. ACS, COS, FGS, FOC, FOS, HRS, HSP, NICMOS, STIS, WFC3, WFPC, WFPC2)
sci_aper_1234	string	Aperture configuration; WFPC2 (e.g. PC1, WF3, WFALL); ACS (e.g. WFC, HRC, SBC); STIS (e.g. 25MAMA, F25QTZ)
sci_spec_1234	string	The filter(s) or grating(s) used (e.g. G160L, G270M, G230LB, F300W)
sci_actual_duration	float	Exposure time in seconds
sci_start_time	datetime	Observation start time; The earliest in-flight data is available from Apr 24 1990

We can refine our results even further with optional keyword arguments. The following parameters are available:

radius: For positional searches only. Only return results within a certain distance from an object or set of coordinates. Default is 3 arcminutes.
limit: The maximum number of results to return. Default is 5000.
offset: Skip the first n results. Useful for paging through results.
sort_by: A list of field names to sort by.
sort_desc: A list of booleans (one for each field specified in sort_by), describing if each field should be sorted in descending order (True) or ascending order (False)
select_cols: A list of columns to be returned in the response.

As we walk through different types of queries, we will see these parameters in action!

Query by Object Name#

We’ve reached our first query! We can use object names to perform metadata queries using the query_object function.

To start, let’s query for the Messier 1 object, a supernova remnant in the Taurus constellation. You may know it better as the Crab Nebula!

# Query for Messier 1 ('M1')
results = missions.query_object('M1')

# Display the first 5 results
print(f'Total number of results: {len(results)}')
results[:5]

Total number of results: 741

Table masked=True length=5

search_pos	sci_data_set_name	sci_targname	sci_hapnum	sci_haspnum	sci_instrume	sci_aper_1234	sci_spec_1234	sci_actual_duration	sci_start_time	sci_pep_id	sci_pi_last_name	sci_ra	sci_dec	sci_refnum	sci_central_wavelength	sci_release_date	sci_stop_time	sci_preview_name	scp_scan_type	sci_hlsp	ang_sep
str15	str9	str30	int64	int64	str6	str17	str26	float64	str26	int64	str12	float64	float64	int64	float64	str26	str26	str9	str18	int64	str19
83.6324 22.0174	J9FX01041	CRAB	1	0	ACS	WFC1-POL120V	F606W;POL120V	2300.0	2005-09-06T21:26:46.433000	10526	HESTER	83.63375381768	22.01859141895	5	5934.7896	2006-09-07T05:15:19.803000	2005-09-06T22:08:13.497000	J9FX01041	--	--	0.10383123118898147
83.6324 22.0174	J9FX02041	CRAB	1	0	ACS	WFC1-POL120V	F606W;POL120V	2300.0	2005-09-15T22:52:34.470000	10526	HESTER	83.63375394433	22.01859153345	5	5934.7886	2006-09-16T09:52:13.417000	2005-09-15T23:34:01.537000	J9FX02041	--	--	0.1038410702845166
83.6324 22.0174	J9FX03041	CRAB	1	0	ACS	WFC1-POL120V	F606W;POL120V	2300.0	2005-09-25T21:05:47.463000	10526	HESTER	83.63375408315	22.0185916581	5	5934.7871	2006-09-26T06:08:15.500000	2005-09-25T21:47:14.527000	J9FX03041	--	--	0.10385181961996841
83.6324 22.0174	J9FX04041	CRAB	1	0	ACS	WFC1-POL120V	F606W;POL120V	2300.0	2005-10-02T22:33:28.457000	10526	HESTER	83.63375418702	22.01859175078	5	5934.7866	2006-10-03T07:13:23.787000	2005-10-02T23:14:55.550000	J9FX04041	--	--	0.10385983838178592
83.6324 22.0174	J9FX05041	CRAB	1	0	ACS	WFC1-POL120V	F606W;POL120V	2000.0	2005-10-12T20:52:39.503000	10526	HESTER	83.63375434754	22.01859189305	5	5934.7852	2006-10-13T02:32:04.710000	2005-10-12T21:29:06.567000	J9FX05041	--	--	0.10387219098736124

There were over 600 total results, meaning that hundreds of HST datasets were targeting the Crab Nebula. Now, let’s try refining our search a bit more.

Each dataset is associated with a celestial coordinate, given by sci_ra (right ascension) and sci_dec (declination). By default, the query returns all datasets that fall within 3 arcminutes from the object’s coordinates. Let’s set the radius parameter to be 1 arcminute instead.
Say that we’re not interested in the first 4 results. We can assign offset to skip a certain number of rows.
By default, a subset of recommended columns are returned for each query. However, we can specify exactly which columns to return using the select_cols keyword argument. Certain columns are included automatically, depending on the mission.

# Refined query for Messier 1 ('M1')
results = missions.query_object('M1',
                                radius=1,  # Search within a 1 arcminute radius
                                offset=4,  # Skip the first 4 results
                                select_cols=['sci_start_time', 'sci_pi_last_name'])  # Select certain columns

# Display the first 5 results
print(f'Total number of results: {len(results)}')
results[:5]

Total number of results: 453

Table masked=True length=5

search_pos	sci_data_set_name	sci_targname	sci_start_time	sci_pi_last_name	ang_sep
str15	str9	str22	str26	str12	str19
83.6324 22.0174	J9FX05041	CRAB	2005-10-12T20:52:39.503000	HESTER	0.10387219098736124
83.6324 22.0174	J9FX06041	CRAB	2005-10-22T20:40:28.510000	HESTER	0.10388689143244417
83.6324 22.0174	J9FX07041	CRAB	2005-10-30T20:36:14.527000	HESTER	0.10390159755961832
83.6324 22.0174	J9FX01011	CRAB	2005-09-06T16:41:38.433000	HESTER	0.10392027927762577
83.6324 22.0174	J9FX01021	CRAB	2005-09-06T18:16:07.427000	HESTER	0.10392027927762577

Query by Region#

The missions object also allows us to query by a region in the sky. By passing in a set of coordinates to the query_region function, we can return datasets that fall within a certain radius value of that point. This type of search is also known as a cone search.

# Create coordinate object
coords = SkyCoord(210.80227, 54.34895, unit=('deg'))

# Query for results within 10 arcseconds of coordinates
results = missions.query_region(coords, 
                                radius=10 * u.arcsec)

# Display results
print(f'Total number of results: {len(results)}')
results[:5]

Total number of results: 19

Table masked=True length=5

search_pos	sci_data_set_name	sci_targname	sci_hapnum	sci_haspnum	sci_instrume	sci_aper_1234	sci_spec_1234	sci_actual_duration	sci_start_time	sci_pep_id	sci_pi_last_name	sci_ra	sci_dec	sci_refnum	sci_central_wavelength	sci_release_date	sci_stop_time	sci_preview_name	scp_scan_type	sci_hlsp	ang_sep
str18	str9	str16	int64	int64	str6	str4	str5	float64	str26	int64	str8	float64	float64	int64	float64	str19	str26	str9	str1	int64	str20
210.80227 54.34895	OBQU01050	NUCLEUS+HODGE602	0	0	STIS	52X2	G140L	186.0	2012-05-24T07:51:40.553000	12556	GORDON	210.8018512676	54.34879151526	1	1425.0	2013-05-24T10:09:14	2012-05-24T07:54:46.553000	OBQU01050	--	--	0.017460048037303017
210.80227 54.34895	OBQU010H0	NUCLEUS+HODGE602	0	0	STIS	52X2	G230L	186.0	2012-05-24T09:17:38.570000	12556	GORDON	210.8018512676	54.34879151526	1	2376.0	2013-05-24T13:15:15	2012-05-24T09:20:44.570000	OBQU010H0	--	--	0.017460048037303017
210.80227 54.34895	OBQU01030	NUCLEUS+HODGE602	0	0	STIS	52X2	G140L	186.0	2012-05-24T07:43:20.553000	12556	GORDON	210.8020002958	54.34928391272	1	1425.0	2013-05-24T10:08:32	2012-05-24T07:46:26.553000	OBQU01030	--	--	0.022143836477276503
210.80227 54.34895	OBQU010F0	NUCLEUS+HODGE602	0	0	STIS	52X2	G230L	186.0	2012-05-24T09:09:18.570000	12556	GORDON	210.8020002958	54.34928391272	1	2376.0	2013-05-24T13:15:05	2012-05-24T09:12:24.570000	OBQU010F0	--	--	0.022143836477276503
210.80227 54.34895	W1000501T	NGC5457-NUC	0	0	WFPC	P6	F555W	100.0	1992-07-16T06:31:16.517000	3639	WESTPHAL	210.802865	54.34933666666667	14	5479.0	1993-07-16T13:33:26	1992-07-16T06:32:56.517000	W1000501T	--	--	0.031163986021028572

The above datasets fall within our cone search. In other words, their target coordinates are within 10 arcseconds of the coordinates that we defined.

Query by Criteria#

In some cases, we may want to run queries with non-positional parameters. To accomplish this, we use the query_criteria function.

For any of our query functions, we can filter our results by the value of columns in the dataset.

Let’s say that we want observations from HST’s Wide Field Camera 3 (WFC3) instument that use the F555W filter. We are only interested in datasets connected to proposal number 15879.

# Query with column criteria
results = missions.query_criteria(sci_instrume='WFC3',  # From Wide Field Camera 3
                                  sci_spec_1234='F555W',  # Uses F555W filter
                                  sci_pep_id=15879,  # Proposal number 15879
                                  select_cols=['sci_instrume', 'sci_spec_1234', 'sci_pep_id', 'sci_pi_last_name'])

# Display the first 5 results
print(f'Total number of results: {len(results)}')
results[:5]

Total number of results: 98

Table masked=True length=5

sci_data_set_name	sci_instrume	sci_spec_1234	sci_pep_id	sci_pi_last_name
str9	str6	str5	int64	str5
IE37NAHHQ	WFC3	F555W	15879	RIESS
IE37NAHIQ	WFC3	F555W	15879	RIESS
IE37NBARQ	WFC3	F555W	15879	RIESS
IE37NBASQ	WFC3	F555W	15879	RIESS
IE37NBATQ	WFC3	F555W	15879	RIESS

To exclude and filter out a certain value from the results, we can prepend the value with !.

Let’s run the same query as above, but this time, we will filter out datasets that use the F555W filter.

# Filtered query, excluding datasets using F555W filter
results = missions.query_criteria(sci_instrume='WFC3', 
                                  sci_spec_1234='!F555W',  # Excludes datasets that use F555W filter
                                  sci_pep_id=15879,
                                  select_cols=['sci_instrume', 'sci_spec_1234', 'sci_pep_id', 'sci_pi_last_name'])

# Display the first 5 results
print(f'Total number of results: {len(results)}')
results[:5]

Total number of results: 236

Table masked=True length=5

sci_data_set_name	sci_instrume	sci_spec_1234	sci_pep_id	sci_pi_last_name
str9	str6	str5	int64	str5
IE37NAHAQ	WFC3	F153M	15879	RIESS
IE37NAHEQ	WFC3	F153M	15879	RIESS
IE37NAHFQ	WFC3	F160W	15879	RIESS
IE37NAHGQ	WFC3	F814W	15879	RIESS
IE37NAHJQ	WFC3	F814W	15879	RIESS

We can also use wildcards on string criteria for more advanced filtering. Wildcards are special characters used in search patterns to represent one or more unknown characters, allowing for flexible matching of strings. The wildcard character is *: it replaces any number of characters preceding, following, or in between the existing characters, depending on its placement.

Let’s use the same query from above, but we will add the condition that the target name must contain the string “GEM”.

# Filtered query with wildcard
results = missions.query_criteria(sci_instrume='WFC3', 
                                  sci_spec_1234='!F555W',
                                  sci_pep_id=15879,
                                  sci_targname='*GEM*',  # Must contain the string 'GEM'
                                  select_cols=['sci_instrume', 'sci_spec_1234', 'sci_pep_id', 'sci_pi_last_name'])

# Display the first 5 results
print(f'Total number of results: {len(results)}')
results[:5]

Total number of results: 5

Table masked=True length=5

sci_data_set_name	sci_targname	sci_instrume	sci_spec_1234	sci_pep_id	sci_pi_last_name
str9	str7	str6	str5	int64	str5
IE37OBBWQ	V-W-GEM	WFC3	F153M	15879	RIESS
IE37OBCGQ	V-W-GEM	WFC3	F153M	15879	RIESS
IE37OBCHQ	V-W-GEM	WFC3	F160W	15879	RIESS
IE37OBCIQ	V-W-GEM	WFC3	F814W	15879	RIESS
IE37OBCLQ	V-W-GEM	WFC3	F814W	15879	RIESS

To filter by multiple values for a single column, we use a string of the values delimited by commas.

To illustrate this, we will use a slightly different query. We query for WFC3 datasets from proposal 15879 that use either the F153M filter or the F160W filter.

# Filtered query with multiple values
results = missions.query_criteria(sci_instrume='WFC3', 
                                  sci_spec_1234='F153M, F160W',  # Uses either F153M filter OR F160W filter
                                  sci_pep_id=15879,
                                  select_cols=['sci_instrume', 'sci_spec_1234', 'sci_pep_id', 'sci_pi_last_name'])

# Display the first 5 results
print(f'Total number of results: {len(results)}')
results[:5]

Total number of results: 138

Table masked=True length=5

sci_data_set_name	sci_instrume	sci_spec_1234	sci_pep_id	sci_pi_last_name
str9	str6	str5	int64	str5
IE37NAHAQ	WFC3	F153M	15879	RIESS
IE37NAHEQ	WFC3	F153M	15879	RIESS
IE37NAHFQ	WFC3	F160W	15879	RIESS
IE37NBAMQ	WFC3	F160W	15879	RIESS
IE37NBANQ	WFC3	F160W	15879	RIESS

For columns with numeric or date values, we can filter using comparison values:

<: Return values less than or before the given number/date
>: Return values greater than or after the given number/date
<=: Return values less than or equal to the given number/date
>=: Return values greater than or equal to the given number/date

As an example, let’s write a query to return all datasets with an observation date before May 1, 1990. These were some of Hubble’s first observations! We’ll use the optional sort_by and sort_desc keywords to sort our results in reverse chronological order.

# Query using comparison operator
results = missions.query_criteria(sci_start_time='<1990-05-01',  # Must be observed before May 1, 1990
                                  select_cols=['sci_start_time', 'sci_pep_id'],
                                  sort_by=['sci_start_time'],  # Sort by observation start time
                                  sort_desc=[True])  # Sort in descending order

# Display the first 10 results
print(f'Total number of results: {len(results)}')
results[:10]

Total number of results: 196

Table masked=True length=10

sci_data_set_name	sci_start_time	sci_pep_id
str9	str26	int64
W0340Y01R	1990-04-24T23:18:15.253000	1476
X1680B01T	1990-04-24T23:17:59.053000	4107
X1681201T	1990-04-24T23:17:59.053000	4107
X16I1N01T	1990-04-24T23:17:59.053000	3801
X14W0401T	1990-04-24T23:17:59.053000	3504
X14W0402T	1990-04-24T23:17:59.053000	3504
X14W0403T	1990-04-24T23:17:59.053000	3504
X14W0404T	1990-04-24T23:17:59.053000	3504
X14W0405T	1990-04-24T23:17:59.053000	3504
X14W0406T	1990-04-24T23:17:59.053000	3504

For numeric or date data types, we can also filter with ranges. This requires the following syntax: '#..#'.

Let’s write a query that uses range syntax to return datasets that have an exposure time between 5000 and 5005 seconds.

# Query using range operator
results = missions.query_criteria(sci_actual_duration='5000..5005',  # Exposure duration is between 5000 and 5005 seconds
                                  select_cols=['sci_pep_id', 'sci_actual_duration'])

# Display results
print(f'Total number of results: {len(results)}')
results[:10]

Total number of results: 133

Table masked=True length=10

sci_data_set_name	sci_actual_duration	sci_pep_id
str9	float64	int64
LC1203020	5000.768	12936
J95S15010	5000.0	10418
IC2B02090	5005.0	12875
IC3A05040	5000.0	13018
IC3A06040	5000.0	13018
IC3A07040	5000.0	13018
IE9T01020	5000.0	16274
IE9T02020	5000.0	16274
IE9T03020	5000.0	16274
IE9T04020	5000.0	16274

Wow, there’s a lot of tips and tricks for writing queries! Here’s a quick summary:

To exclude and filter out a certain value from the results, prepend the value with !.
Wildcards are special characters used in search patterns to represent one or more unknown characters, allowing for flexible matching of strings. The wildcard character is * and it replaces any number of characters preceding, following, or in between existing characters, depending on its placement.
To filter by multiple values for a single column, use a string of values delimited by commas.
For columns with numeric or date data types, filter using comparison values (<, >, <=, >=).
For columns with numeric or date data types, select a range with the syntax '#..#'.

Getting Data Products#

Performing a Product Query#

Each observation returned from a MAST query can have one or more associated data products. For example, a JWST observation might return an uncalibrated file, a guide-star file, and the actual science data.

For reproducibility, we’ll run another criteria query for datasets that use Hubble’s Advanced Camera for Surveys (ACS) instrument. We are interested in datasets connected to proposal number 12451 that are associated with at least one High Level Science Product.

# Query using range operator
datasets = missions.query_criteria(sci_pep_id=12451,  # Proposal number 12451
                                   sci_instrume='ACS',  # Use ACS instrument
                                   sci_hlsp='>1')  # Associated with at least one HLSP

# Display results
print(f'Total number of results: {len(datasets)}')
datasets[:5]

Total number of results: 29

Table masked=True length=5

sci_data_set_name	sci_targname	sci_hapnum	sci_haspnum	sci_instrume	sci_aper_1234	sci_spec_1234	sci_actual_duration	sci_start_time	sci_pep_id	sci_pi_last_name	sci_ra	sci_dec	sci_refnum	sci_central_wavelength	sci_release_date	sci_stop_time	sci_preview_name	scp_scan_type	sci_hlsp
str9	str18	int64	int64	str6	str7	str14	float64	str26	int64	str7	float64	float64	int64	float64	str19	str26	str9	str1	int64
JBTAA0010	ABELL209	1	0	ACS	WFC-FIX	F625W;CLEAR2L	1032.0	2012-06-28T14:11:54.653000	12451	POSTMAN	22.96891666667	-13.61122222222	36	6311.8481	2012-06-28T17:17:47	2012-06-28T14:31:45.717000	JBTAA0010	--	12
JBTAA0020	ABELL209	1	0	ACS	WFC-FIX	F850LP;CLEAR2L	1035.0	2012-06-28T14:34:44.670000	12451	POSTMAN	22.96887414019	-13.61118826361	36	9031.459	2012-06-28T17:47:50	2012-06-28T14:55:15.703000	JBTAA0020	--	12
JBTAA1010	ABELL209	1	0	ACS	WFC-FIX	F475W;CLEAR2L	1032.0	2012-06-28T15:47:39.677000	12451	POSTMAN	22.96891666667	-13.61122222222	36	4746.9565	2012-06-28T19:30:26	2012-06-28T16:07:30.710000	JBTAA1010	--	12
JBTAA1020	ABELL209	1	0	ACS	WFC-FIX	F775W;CLEAR2L	1031.0	2012-06-28T16:10:33.660000	12451	POSTMAN	22.96887414019	-13.61118826361	36	7693.4688	2012-06-28T19:10:46	2012-06-28T16:31:00.693000	JBTAA1020	--	12
JBTAA3010	ABELL209	1	0	ACS	WFC-FIX	F606W;CLEAR2L	1032.0	2012-07-15T16:19:51.680000	12451	POSTMAN	22.96891666667	-13.61122222222	36	5921.8911	2012-07-15T19:08:21	2012-07-15T16:39:42.713000	JBTAA3010	--	12

The get_product_list function accepts a table of datasets or a list of dataset IDs and returns a table containing the associated data products. Let’s fetch the data products for the first three datasets in the table above.

# Get a list of data products
products = missions.get_product_list(datasets[:3])

# Display results
print(f'Total number of products: {len(products)}')
products[:5]

Total number of products: 309

Table length=5

product_key	access	dataset	instrument_name	filters	filename	uri	authz_primary_identifier	authz_secondary_identifier	file_suffix	category	size	type
str66	str6	str9	str6	str14	str56	str66	str42	str3	str15	str14	int64	str9
JBTAA0010_jbtaa0010_asn.fits	PUBLIC	JBTAA0010	ACS	F625W;CLEAR2L	jbtaa0010_asn.fits	JBTAA0010/jbtaa0010_asn.fits	JBTAA0010	ASN	ASN	AUX	11520	science
JBTAA0010_jbtaa0010_trl.fits	PUBLIC	JBTAA0010	ACS	F625W;CLEAR2L	jbtaa0010_trl.fits	JBTAA0010/jbtaa0010_trl.fits	JBTAA0010	CAL	TRL	AUX	679680	science
JBTAA0010_jbtaa0010_drc.fits	PUBLIC	JBTAA0010	ACS	F625W;CLEAR2L	jbtaa0010_drc.fits	JBTAA0010/jbtaa0010_drc.fits	JBTAA0010	CAL	DRC	CALIBRATED	214660800	science
JBTAA0010_jbtaa0010_spt.fits	PUBLIC	JBTAA0010	ACS	F625W;CLEAR2L	jbtaa0010_spt.fits	JBTAA0010/jbtaa0010_spt.fits	JBTAA0010	CAL	SPT	UNCALIBRATED	86400	science
JBTAA0010_jbtaa0010_log.txt	PUBLIC	JBTAA0010	ACS	F625W;CLEAR2L	jbtaa0010_log.txt	JBTAA0010/jbtaa0010_log.txt	JBTAA0010	CAL	LOG	AUX	224739	science

Some products can be associated with multiple datasets, and this table may contain duplicates. To return a list of products with only unique filenames, use the get_unique_product_list function.

# Get products with unique filenames
unique_products = missions.get_unique_product_list(datasets[:3])

# Display results
unique_products[:5]

INFO: 26 of 309 products were duplicates. Only returning 283 unique product(s). [astroquery.mast.utils]
INFO: To return all products, use `MastMissions.get_product_list` [astroquery.mast.missions]

Table length=5

product_key	access	dataset	instrument_name	filters	filename	uri	authz_primary_identifier	authz_secondary_identifier	file_suffix	category	size	type
str66	str6	str9	str6	str14	str56	str66	str42	str3	str15	str14	int64	str9
JBTAA0010_17717071j_osc.fits	PUBLIC	JBTAA0010	ACS	F625W;CLEAR2L	17717071j_osc.fits	JBTAA0010/17717071j_osc.fits			OSC	REFERENCE	17280	reference
JBTAA0010_25g1256nj_bpx.fits	PUBLIC	JBTAA0010	ACS	F625W;CLEAR2L	25g1256nj_bpx.fits	JBTAA0010/25g1256nj_bpx.fits			BPX	REFERENCE	23040	reference
JBTAA0010_37g1550cj_mdz.fits	PUBLIC	JBTAA0010	ACS	F625W;CLEAR2L	37g1550cj_mdz.fits	JBTAA0010/37g1550cj_mdz.fits			MDZ	REFERENCE	247680	reference
JBTAA0010_4af1559ij_imp.fits	PUBLIC	JBTAA0010	ACS	F625W;CLEAR2L	4af1559ij_imp.fits	JBTAA0010/4af1559ij_imp.fits			IMP	REFERENCE	953280	reference
JBTAA0010_4bb1536cj_idc.fits	PUBLIC	JBTAA0010	ACS	F625W;CLEAR2L	4bb1536cj_idc.fits	JBTAA0010/4bb1536cj_idc.fits			IDC	REFERENCE	285120	reference

Filtering Data Products#

These datasets returned quite a few products! We are not interested in all of them, and luckily, we have a handy function to filter them for us. filter_products allows you to filter based on file extension (extension) and any other of the product fields.

A quick note on filtering: the AND operation is performed for a list of filters, and the OR operation is performed within a filter set. For example, the filter below will return FITS products that are “science” type and have a file_suffix of “ASN” (association files) or “JIF” (jitter information files).

# Filter products 
filtered = missions.filter_products(products,
                                    extension='fits',  # FITS file extension
                                    type='science',  # Science data
                                    file_suffix=['ASN', 'JIF'])  # Association files OR jitter information files

# Display results
filtered

Table length=6

product_key	access	dataset	instrument_name	filters	filename	uri	authz_primary_identifier	authz_secondary_identifier	file_suffix	category	size	type
str66	str6	str9	str6	str14	str56	str66	str42	str3	str15	str14	int64	str9
JBTAA0010_jbtaa0010_asn.fits	PUBLIC	JBTAA0010	ACS	F625W;CLEAR2L	jbtaa0010_asn.fits	JBTAA0010/jbtaa0010_asn.fits	JBTAA0010	ASN	ASN	AUX	11520	science
JBTAA0010_jbtaa0010_jif.fits	PUBLIC	JBTAA0010	ACS	F625W;CLEAR2L	jbtaa0010_jif.fits	JBTAA0010/jbtaa0010_jif.fits	JBTAA0010	OMS	JIF	JITTER/SUPPORT	60480	science
JBTAA0020_jbtaa0020_asn.fits	PUBLIC	JBTAA0020	ACS	F850LP;CLEAR2L	jbtaa0020_asn.fits	JBTAA0020/jbtaa0020_asn.fits	JBTAA0020	ASN	ASN	AUX	11520	science
JBTAA0020_jbtaa0020_jif.fits	PUBLIC	JBTAA0020	ACS	F850LP;CLEAR2L	jbtaa0020_jif.fits	JBTAA0020/jbtaa0020_jif.fits	JBTAA0020	OMS	JIF	JITTER/SUPPORT	60480	science
JBTAA1010_jbtaa1010_asn.fits	PUBLIC	JBTAA1010	ACS	F475W;CLEAR2L	jbtaa1010_asn.fits	JBTAA1010/jbtaa1010_asn.fits	JBTAA1010	ASN	ASN	AUX	11520	science
JBTAA1010_jbtaa1010_jif.fits	PUBLIC	JBTAA1010	ACS	F475W;CLEAR2L	jbtaa1010_jif.fits	JBTAA1010/jbtaa1010_jif.fits	JBTAA1010	OMS	JIF	JITTER/SUPPORT	60480	science

Downloading Products#

The download_products function accepts a table of products like the one above and will download the products to your local machine. By default, products will be downloaded into the current working directory, in a subdirectory called mastDownload. The full local filepaths will have the form mastDownload/<mission>/<Dataset ID>/file. You can change the download directory using the download_dir parameter.

# Download products using filtered product Table
manifest = missions.download_products(filtered[:2])

# Display results
manifest

Downloading URL https://mast.stsci.edu/search/hst/api/v0.1/retrieve_product?product_name=JBTAA0010%2Fjbtaa0010_asn.fits to mastDownload/hst/JBTAA0010/jbtaa0010_asn.fits ...

 [Done]

Downloading URL https://mast.stsci.edu/search/hst/api/v0.1/retrieve_product?product_name=JBTAA0010%2Fjbtaa0010_jif.fits to mastDownload/hst/JBTAA0010/jbtaa0010_jif.fits ...

 [Done]

Table length=2

Local Path	Status	Message	URL
object	str8	object	object
mastDownload/hst/JBTAA0010/jbtaa0010_asn.fits	COMPLETE	None	None
mastDownload/hst/JBTAA0010/jbtaa0010_jif.fits	COMPLETE	None	None

For a more streamlined workflow, the function also accepts dataset IDs and product filters.

# Download products using dataset IDs and product filters
manifest = missions.download_products(['JBTAA0010', 'JBTAA0020'],
                                      extension='fits',
                                      type='science',
                                      file_suffix=['ASN', 'JIF'])

# Display results
manifest

INFO: Found cached file mastDownload/hst/JBTAA0010/jbtaa0010_asn.fits with expected size 11520. [astroquery.query]

INFO: Found cached file mastDownload/hst/JBTAA0010/jbtaa0010_jif.fits with expected size 60480. [astroquery.query]

Downloading URL https://mast.stsci.edu/search/hst/api/v0.1/retrieve_product?product_name=JBTAA0020%2Fjbtaa0020_asn.fits to mastDownload/hst/JBTAA0020/jbtaa0020_asn.fits ...

 [Done]

Downloading URL https://mast.stsci.edu/search/hst/api/v0.1/retrieve_product?product_name=JBTAA0020%2Fjbtaa0020_jif.fits to mastDownload/hst/JBTAA0020/jbtaa0020_jif.fits ...

 [Done]

Table length=4

Local Path	Status	Message	URL
object	str8	object	object
mastDownload/hst/JBTAA0010/jbtaa0010_asn.fits	COMPLETE	None	None
mastDownload/hst/JBTAA0010/jbtaa0010_jif.fits	COMPLETE	None	None
mastDownload/hst/JBTAA0020/jbtaa0020_asn.fits	COMPLETE	None	None
mastDownload/hst/JBTAA0020/jbtaa0020_jif.fits	COMPLETE	None	None

To download a single data product file, use the download_file function with a MAST URI as input. The default is to download the file to the current working directory, but you can specify the download directory or filepath with the local_path keyword argument.

# Download a single data product
result = missions.download_file('JBTAA0010/jbtaa0010_asn.fits')

# Display result
result

Downloading URL https://mast.stsci.edu/search/hst/api/v0.1/retrieve_product?product_name=JBTAA0010%2Fjbtaa0010_asn.fits to jbtaa0010_asn.fits ...

 [Done]

('COMPLETE', None, None)

Exclusive Data Access#

Some data may not be publicly available and will require authentication and authorization. To download proprietary data with Astroquery, you will need a MyST Account with proper permissions. You will also need to provide an API token.

You can use the login function to authenticate yourself. After uncommenting and executing the following cell, you should be prompted to enter your token.

# missions.login()

You can also provide a token to a MastMissions object upon initialization using the mast_token parameter. However, remember to be cautious with your API token. You should not share the token or check it into source control. For the best security, we recommend using the login method to authenticate yourself.

Switching Missions#

As mentioned previously, each MastMissions object can only make queries and download products from a single collection at a time. This collection can be modified with the mission class attribute, which is case-insensitive. This allows users to query multiple collections with the same object.

To demonstrate, we’ll create a new MastMissions object and initialize the mission to be 'JWST'. This will perform queries on dataset metadata from the James Webb Space Telescope.

multi_mission = MastMissions(mission='JWST')
multi_mission.mission

'jwst'

Next, we’ll query for JWST datasets around NGC 346, a young star cluster in the Small Magellanic Cloud. We’ll use a radius of 0.2 arcminutes.

# Query JWST for NGC 346
results = multi_mission.query_object('NGC 346',
                                     radius=0.2)  # Search within a 0.2 arcminute radius

# Display results
print(f'Total number of datasets: {len(results)}')
results[:5]

Total number of datasets: 169

Table masked=True length=5

ArchiveFileID	fileSetName	productLevel	targprop	targ_ra	targ_dec	instrume	exp_type	opticalElements	date_obs	duration	program	observtn	visit	publicReleaseDate	pi_name	proposal_type	proposal_cycle	targtype	access	ang_sep
int64	str25	str14	str22	float64	float64	str7	str13	str76	str27	float64	int64	int64	int64	str19	str17	str3	int64	str5	str16	float64
143912893	jw01227001003_02101_00001	1b, 2a, 2b, 2c	NGC-346	14.77060458333333	-72.16920833333336	MIRI	MIR_IMAGE	F770W	2022-10-10T07:43:23.1330000	77.701	1227	1	3	2023-10-11T03:40:56	Meixner, Margaret	GTO	1	FIXED	PUBLIC	0.0
143912790	jw01227001003_02101_00002	1b, 2a, 2b, 2c	NGC-346	14.77060458333333	-72.16920833333336	MIRI	MIR_IMAGE	F770W	2022-10-10T07:46:04.0930000	77.701	1227	1	3	2023-10-11T03:39:18	Meixner, Margaret	GTO	1	FIXED	PUBLIC	0.0
143912742	jw01227001003_02101_00003	1b, 2a, 2b, 2c	NGC-346	14.77060458333333	-72.16920833333336	MIRI	MIR_IMAGE	F770W	2022-10-10T07:48:42.3010000	77.701	1227	1	3	2023-10-11T03:39:23	Meixner, Margaret	GTO	1	FIXED	PUBLIC	0.0
143912909	jw01227001003_02101_00004	1b, 2a, 2b, 2c	NGC-346	14.77060458333333	-72.16920833333336	MIRI	MIR_IMAGE	F770W	2022-10-10T07:51:20.4450000	77.701	1227	1	3	2023-10-11T03:40:28	Meixner, Margaret	GTO	1	FIXED	PUBLIC	0.0
143912806	jw01227001003_02103_00001	1b, 2a, 2b, 2c	NGC-346	14.77060458333333	-72.16920833333336	MIRI	MIR_IMAGE	F1000W	2022-10-10T07:56:20.1650000	99.901	1227	1	3	2023-10-11T03:43:08	Meixner, Margaret	GTO	1	FIXED	PUBLIC	0.0

This query returned over 160 JWST datasets. Now, let’s try it with a different data collection. We’ll reassign the mission attribute on the multi_mission object to be 'ullyses' and run the same query.

multi_mission.mission = 'ullyses'
multi_mission.mission

'ullyses'

# Query ULLYSES for NGC 346
results = multi_mission.query_object('NGC 346',
                                     radius=0.2)  # Search within a 0.2 arcminute radius

# Display results
print(f'Total number of datasets: {len(results)}')
results[:5]

Total number of datasets: 2

Table masked=True length=2

search_pos	target_id	names_search	target_name_hlsp	simbad_link	target_classification	targ_ra	targ_dec	host_galaxy_name	spectral_type	bmv0_mag	u_mag	b_mag	v_mag	gaia_g_mean_mag	star_mass	instrument	grating	filter	observation_id	ang_sep
str17	int64	str48	str14	str66	str12	float64	float64	str3	str4	float64	float64	float64	float64	float64	float64	str4	str5	str1	str39	str19
14.76833 -72.1775	55	NGC346 MPG 396,Cl* NGC346 MPG 396,NGC346-MPG-396	NGC346-MPG-396	https://simbad.u-strasbg.fr/simbad/sim-id?Ident=Cl*+NGC346+MPG+396	Mid O Dwarf	14.762184457428036	-72.17637442586573	SMC	O7 V	-0.27	13.09	14.17	14.39	14.382007	--	COS	G160M	--	hlsp_ullyses_hst_cos_ngc346-mpg-396_uv	0.13152388757468492
14.76833 -72.1775	57	NGC346 MPG 487,Cl* NGC346 MPG 487,NGC346-MPG-487	NGC346-MPG-487	https://simbad.u-strasbg.fr/simbad/sim-id?Ident=Cl*+NGC346+MPG+487	Late O Dwarf	14.778136436564196	-72.17813531865009	SMC	O8 V	-0.27	13.3	14.31	14.53	14.499837	25.7	STIS	E140M	--	hlsp_ullyses_hst_stis_ngc346-mpg-487_uv	0.18407398702435046

Notice that this query returned only a few datasets. The result tables also look very different in terms of data and column keywords. This is because each query is being performed on a different data collection!

Exercises#

Exercise 1: It’s time to apply all that you’ve learned and try your hand at writing a MastMissions query! Write a non-positional query based on the following:

Image observations
Instrument should NOT include the Cosmic Origins Spectrograph (COS)
Filter used is F150W, F105W, or F110W
Declination is greater than 0 degrees
Exposure time is between 1000 and 2000 seconds
Target name contains the string “GAL”
Skip the first 5 entries
Sort by exposure time in descending order
Limit the results to 3 datasets

# A non-positional query with column criteria
# results = missions.query_criteria(...)  # Write your query here!

# Display results
# results

Exercise 2: Using your results from Exercise 1, download the association table data products for the 3 datasets (HINT: file_suffix = 'ASN'). You can fetch, filter, and download the products as three separate steps, or use the streamlined workflow built in to download_products.

# Fetch products from 3 datasets
# products = missions.get_product_list(...)

# Filter products
# filtered = missions.filter_products(...)

# Download products
# missions.download_products(...)

Exercise 3: Use a new MastMissions object and the mission attribute to search for datasets around the coordinate “22h57m39s -29d37m20s” from both HST and JWST. Use a radius of 0.1 arcminutes.

# Create new MastMissions object
#m = MastMissions()

# Create sky coordinate object
#coord = SkyCoord(...)

# Query HST metadata for region
#results = m.query_region(...)

# Display the first 5 results
#print(f'Total number of datasets: {len(results)}')
#results[:5]

# Switch mission to JWST
# ...

# Query JWST metadata for region
#results = m.query_region(...)

# Display the first 5 results
#print(f'Total number of datasets: {len(results)}')
#results[:5]

Exercise Solutions#

Exercise 1:

# A non-positional query with column criteria
results = missions.query_criteria(sci_obs_type='IMAGE',
                                  sci_instrume='!COS',
                                  sci_spec_1234='F150W, F105W, F110W',
                                  sci_dec='>0',
                                  sci_actual_duration='1000..2000',
                                  sci_targname='*GAL*',
                                  offset=5,
                                  sort_by=['sci_actual_duration'],
                                  sort_desc=[True],
                                  limit=3)

# Display results
results

WARNING: MaxResultsWarning: Maximum results returned, may not include all sources within radius. [astroquery.mast.missions]

Table masked=True length=3

sci_data_set_name	sci_targname	sci_hapnum	sci_haspnum	sci_instrume	sci_aper_1234	sci_spec_1234	sci_actual_duration	sci_start_time	sci_pep_id	sci_pi_last_name	sci_ra	sci_dec	sci_refnum	sci_central_wavelength	sci_release_date	sci_stop_time	sci_preview_name	scp_scan_type	sci_hlsp
str9	str27	int64	int64	str6	str6	str5	float64	str26	int64	str6	float64	float64	int64	float64	str19	str26	str9	str1	int64
N4A705010	GAL-CLUS-0026+1653-ARCC	0	0	NICMOS	NIC1	F110W	1151.9038	1998-01-08T03:44:07.490000	7425	TURNER	6.657118050129	17.16004527511	0	11292.4	1999-01-08T19:47:33	1998-01-08T04:04:06.490000	N4A705010	--	--
N4A703010	GAL-CLUS-0026+1653-ARCD	0	0	NICMOS	NIC1	F110W	1151.9038	1997-10-31T14:20:31.493000	7425	TURNER	6.64289440364	17.16556874169	0	11292.4	1998-10-31T21:30:52	1997-10-31T14:40:30.493000	N4A703010	--	--
IF9V31030	VIRGO-INTERGALACTIC-FIELD-E	1	0	WFC3	IR-FIX	F110W	1058.805543	2023-12-14T15:40:25.307000	17510	GREGG	187.6896916667	12.99041111111	0	11534.459	2023-12-14T21:58:47	2023-12-14T16:08:42.237000	IF9V31030	--	--

Exercise 2:

# As 3 separate steps
# Fetch products from first 3 datasets
products = missions.get_product_list(results)

# Filter products
filtered = missions.filter_products(products,
                                    file_suffix='ASN')

# Download products
missions.download_products(filtered)

WARNING: MaxResultsWarning: Maximum results returned, may not include all sources within radius. [astroquery.mast.missions]

Downloading URL https://mast.stsci.edu/search/hst/api/v0.1/retrieve_product?product_name=IF9V31030%2Fif9v31030_asn.fits to mastDownload/hst/IF9V31030/if9v31030_asn.fits ...

 [Done]

Downloading URL https://mast.stsci.edu/search/hst/api/v0.1/retrieve_product?product_name=N4A703010%2Fn4a703010_asn.fits to mastDownload/hst/N4A703010/n4a703010_asn.fits ...

 [Done]

Downloading URL https://mast.stsci.edu/search/hst/api/v0.1/retrieve_product?product_name=N4A705010%2Fn4a705010_asn.fits to mastDownload/hst/N4A705010/n4a705010_asn.fits ...

 [Done]

Table length=3

Local Path	Status	Message	URL
object	str8	object	object
mastDownload/hst/IF9V31030/if9v31030_asn.fits	COMPLETE	None	None
mastDownload/hst/N4A703010/n4a703010_asn.fits	COMPLETE	None	None
mastDownload/hst/N4A705010/n4a705010_asn.fits	COMPLETE	None	None

# Streamlined
missions.download_products(results['sci_data_set_name'].tolist(),
                           file_suffix='ASN')

WARNING: MaxResultsWarning: Maximum results returned, may not include all sources within radius. [astroquery.mast.missions]

WARNING: MaxResultsWarning: Maximum results returned, may not include all sources within radius. [astroquery.mast.missions]

WARNING: MaxResultsWarning: Maximum results returned, may not include all sources within radius. [astroquery.mast.missions]

INFO: Found cached file mastDownload/hst/IF9V31030/if9v31030_asn.fits with expected size 11520. [astroquery.query]

INFO: Found cached file mastDownload/hst/N4A703010/n4a703010_asn.fits with expected size 11520. [astroquery.query]

INFO: Found cached file mastDownload/hst/N4A705010/n4a705010_asn.fits with expected size 11520. [astroquery.query]

Table length=3

Local Path	Status	Message	URL
object	str8	object	object
mastDownload/hst/IF9V31030/if9v31030_asn.fits	COMPLETE	None	None
mastDownload/hst/N4A703010/n4a703010_asn.fits	COMPLETE	None	None
mastDownload/hst/N4A705010/n4a705010_asn.fits	COMPLETE	None	None

Exercise 3:

# Create new MastMissions object
m = MastMissions()

# Create sky coordinate object
coord = SkyCoord('22h57m39s -29d37m20s')

# Query HST metadata for region
results = m.query_region(coord,
                         radius=0.1)

# Display the first 5 results
print(f'Total number of datasets: {len(results)}')
results[:5]

Total number of datasets: 476

Table masked=True length=5

search_pos	sci_data_set_name	sci_targname	sci_hapnum	sci_haspnum	sci_instrume	sci_aper_1234	sci_spec_1234	sci_actual_duration	sci_start_time	sci_pep_id	sci_pi_last_name	sci_ra	sci_dec	sci_refnum	sci_central_wavelength	sci_release_date	sci_stop_time	sci_preview_name	scp_scan_type	sci_hlsp	ang_sep
str18	str9	str19	int64	int64	str6	str12	str13	float64	str26	int64	str9	float64	float64	int64	float64	str26	str26	str9	str1	int64	str20
344.4125 -29.62222	N4N92Q040	HD216956	0	0	NICMOS	NIC2-FIX	F222M	255.923	1998-08-06T01:24:53.733000	7894	HENRY	344.4125879875	-29.62214772201	8	22181.699	2000-07-04T20:12:32	1998-08-06T01:29:20.733000	N4N92Q040	--	--	0.006314126839181508
344.4125 -29.62222	N4N92Q030	HD216956	0	0	NICMOS	NIC2-FIX	F207M	255.923	1998-08-06T01:19:59.733000	7894	HENRY	344.4125879884	-29.62214772055	8	20824.1	2000-07-04T20:12:32	1998-08-06T01:24:26.733000	N4N92Q030	--	--	0.00631422112268177
344.4125 -29.62222	N4N92Q020	HD216956	0	0	NICMOS	NIC2-FIX	F180M	127.92868	1998-08-06T01:17:16.733000	7894	HENRY	344.412587989	-29.62214771973	8	17971.1	2000-07-04T20:12:32	1998-08-06T01:19:35.733000	N4N92Q020	--	--	0.006314277659432727
344.4125 -29.62222	N4N92Q010	HD216956	0	0	NICMOS	NIC2-FIX	F110W	127.92868	1998-08-06T01:14:30.733000	7894	HENRY	344.4125879895	-29.62214771891	8	11284.7	2000-07-04T20:12:32	1998-08-06T01:16:49.733000	N4N92Q010	--	--	0.006314330409832213
344.4125 -29.62222	Z3EJ0102T	HD216956	0	0	HRS	2.0	MIRROR-A1	0.0	1996-09-30T08:18:34.937000	6627	LALLEMENT	344.4123659004	-29.6221209983	0	--	1997-09-30T22:45:42	1996-09-30T08:22:46.937000	--	--	--	0.00917640457522127

# Switch mission to JWST
m.mission = 'JWST'

# Query JWST metadata for region
results = m.query_region(coord,
                         radius=0.1)

# Display the first 5 results
print(f'Total number of datasets: {len(results)}')
results[:5]

Total number of datasets: 67

Table masked=True length=5

ArchiveFileID	fileSetName	productLevel	targprop	targ_ra	targ_dec	instrume	exp_type	opticalElements	date_obs	duration	program	observtn	visit	publicReleaseDate	pi_name	proposal_type	proposal_cycle	targtype	access	ang_sep
int64	str25	str14	str25	float64	float64	str6	str13	str48	str27	float64	int64	int64	int64	str19	str20	str3	int64	str5	str16	float64
139989292	jw01193006001_02101_00001	1b, 2a, 2b, 2c	FOMALHAUT	344.4150901092018	-29.62327928996265	MIRI	MIR_IMAGE	F2550W	2022-10-21T20:17:08.6000000	269.102	1193	6	1	2023-10-21T22:14:59	Beichman, Charles A.	GTO	1	FIXED	PUBLIC	0.0
139989097	jw01193006001_02101_00002	1b, 2a, 2b, 2c	FOMALHAUT	344.4150901103719	-29.62327929047184	MIRI	MIR_IMAGE	F2550W	2022-10-21T20:22:59.8950000	269.102	1193	6	1	2023-10-21T22:12:51	Beichman, Charles A.	GTO	1	FIXED	PUBLIC	0.0
139993460	jw01193006001_02101_00003	1b, 2a, 2b, 2c	FOMALHAUT	344.415090111542	-29.62327929098103	MIRI	MIR_IMAGE	F2550W	2022-10-21T20:28:51.1910000	269.102	1193	6	1	2023-10-21T22:12:48	Beichman, Charles A.	GTO	1	FIXED	PUBLIC	0.0
139989372	jw01193006001_02101_00004	1b, 2a, 2b, 2c	FOMALHAUT	344.4150901127151	-29.62327929149152	MIRI	MIR_IMAGE	F2550W	2022-10-21T20:34:43.3830000	269.102	1193	6	1	2023-10-21T22:13:16	Beichman, Charles A.	GTO	1	FIXED	PUBLIC	0.0
139989260	jw01193007001_02101_00001	1b, 2a, 2b	FOMALHAUT-COPY-MIRI-CORON	344.4150901161078	-29.62327929296791	MIRI	MIR_TACQ	FND	2022-10-21T20:53:55.8600000	1.296	1193	7	1	2023-10-22T07:32:01	Beichman, Charles A.	GTO	1	FIXED	PUBLIC	0.0

Additional Resources#

Citations#

If you use astroquery for published research, please cite the authors. Follow these links for more information about citing astroquery:

Citing Astroquery

About this Notebook#

Author(s): Sam Bianco
Keyword(s): Tutorial, Astroquery, MastMissions
First published: January 2025
Last updated: January 2025

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