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Currently the primary access to the PanSTARRS catalog is through database queries using the MAST CasJobs interface, and the primary access to the PS1 images is through the PS1 Image Cutout Service. There is also a simple web form to search the catalog for objects near a sky position and a VO-compatible cone search interface for simple positional searches. A VO TAP service also provides direct SQL query access to the database. Both catalog and image interfaces can be used from scripts. The DR2 search API is fully functional and can be used to search various tables with constraints from any of the table columns. The PS1 images are also accessible via the MAST Portal. Python notebooks are provided with examples of access via various interfaces.
The starting point for the PS1 data archive is at Pan-STARRS1 data archive home page.
Catalog Access Tools
The PanSTARRS catalog is a large database of the objects and measurements of their properties including astrometry, photometry, and morphology. It includes copious metadata including links to the images that were used for the various object measurements. The total database size for the DR2 release is nearly 150 TB.
Web search form
A simple web form searches the object catalog for objects near a search position.
Features of the https://catalogs.mast.stsci.edu/ web form:
- The form includes a name resolver allowing positions to be specified using object names recognized by SIMBAD or NED.
- The form can search multiple tables from the catalog, including mean objects, stack objects, and detections. Additional tables will be added in the near future.
- A list of target names or positions can be uploaded to search multiple positions at once. (Currently this is relatively slow, but enhancements coming soon will make it much faster.)
- The user can customize the columns returned from the search and can add additional constraints on the search (e.g., using magnitudes or other columns from the catalogs).
- We recommend using the nDetections>=2 constraint when searching the mean objects catalog to reduce the number of spurious objects in the catalog. Such object are usually detected at only a single epoch.
- The search results from this form can be view interactively or downloaded as a comma-separated value file.
- Note: The old web form at http https://archive.stsci.edu/panstarrs/search.php remains available but can only be used to search the DR1 catalog. It also lacks many of the filtering capabilities of the new interface. We recommend that all users of the old interface switch to the new interface. Eventually the old interface will be retired.
Catalogs API for scripted searches
The search form has an associated application program interface (API) that can be used for programmatic (scripted) queries to the catalog. The API includes access to all the functionality of the interface, including constraints, selection of columns, etc. It can return the data in various formats including CSV, JSON, VOTables. It also has a metadata query to get a list of the available columns for a particular table. The API can be used for larger queries than the web interface, which is limited to returning 50,000 rows of data.
It is simple to use the API either from a language such as Python or with other command-line tools such as curl.
See our Python Jupyter notebook for the MAST API for simple examples of how to access the PS1 DR2 catalog via the API.
Here is a simple 1-line command using curl to get a csv table of source information from the mean catalog. This searches the PS1 DR2 mean catalog for sources within 0.5 degrees of the position of NGC 6822. It restricts the result to objects with nDetections >= 2 and increases the default limit on the number of sources returned to 500,000. This query executes in less than one minute:
curl 'https://catalogs.mast.stsci.edu/api/v0.1/panstarrs/dr2/mean.csv?ra=296.24059&dec=-14.80343&radius=0.5&pagesize=500000&nDetections.gte=2' > ngc6822.csv
Easy cross-match using catalogs API
The MAST catalogs interface has a cross-match capability that can be easily used to match lists of source positions. See Easy cross-match with PS1 using a list of source positions for an example.
VO cone search
- Mean object search httphttps://gsss.stsci.edu/webservices/vo/CatalogSearch.aspx?CAT=PS1dr2OBJECTS&RA=<ra>&DEC=<dec-degrees>&SR=<radius-degrees>
- Detection search httphttps://gsss.stsci.edu/webservices/vo/CatalogSearch.aspx?CAT=PS1dr2DETECTIONS&RA=<ra>&DEC=<dec-degrees>&SR=<radius-degrees>
- A generic description of this multi-catalog service is available at httphttps://gsss.stsci.edu/Software/WebServices.htm.
SQL query interface: MAST CasJobs
- The CasJobs interface (which is familiar to users of SDSS) includes built-in Help along with some sample PS1 queries. It also includes links to SQL tutorials.
- The MAST CasJobs interface also provides access to other catalogs including GALEX, Kepler, and the Hubble Source Catalog.
- Note that CasJobs queries can also be executed from Python. See our Python Jupyter notebook for PS1 CasJobs queries for examples of one approach to accessing CasJobs from Python. Note that this notebook does the same queries as the MAST API notebook, so a comparison between the two may help in understanding the relationship between the two interfaces.
SQL query interface: VO TAP
- The VO Table Access Protocol (TAP) interface allows database queries through a SQL-based language. The MAST PS1 TAP interface can be used to query the PS1 database. See the TAP documentation for details about how TAP services work.
- A Jupyter notebook shows how to query the database from Python.
- The PS1 TAP service is also accessible from standard VO tools such as TOPCAT. In TOPCAT, use the VO → Table Access Protocol menu to get a list of list of services, and then search for PS1DR2 to find the TAP service.
Image Access Tools
PS1 Image Cutout Service
- The PS1 Image Cutout Service provides color images, JPEG and FITS cutouts, an interactive image display, and full FITS image retrievals.
- The image cutout service is programmatically accessible to allow downloading either cutouts or full images via a script. There is a simple Python Jupyter notebook that shows how to download images.
|Data Retrieval Quick Links|
|Catalog and Database Access|
|VO Cone Search|
|Image cutout service||https://ps1images.stsci.edu/cgi-bin/ps1cutouts|