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The Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) is an wide-field astronomical imaging and data processing facility developed at the University of Hawaii's Institute for Astronomy. Pan-STARRS1 (PS1) is the first telescope of Pan-STARRS to be completed and is the basis for DR1 and DR2. The PS1 survey used a 1.8 meter telescope and its 1.4 Gigapixel camera (GPC1; see PS1 GPC1 camera) to image the sky in five broadband filters (g, r, i, z, y). The PS1 Science Consortium funded the operation of the Pan-STARRS1 telescope, situated at Haleakala Observatories near the summit of Haleakala in Hawaii, from 2010 to 2014, and the data released in DR1 and DR2 are based on these observations. 

What types of data were obtained? 

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How does PS1 compare to other surveys such as the Sloan Digital Sky Survey (SDSS)?

Metcalfe et al.   MNRAS 435, 1825 (2013) - has a detailed comparison of the PS1 Small Area Survey 2 data (a test 3pi region taken under optimal conditions) with SDSS. Here are plots comparing 5-sigma counts from SDSS DR8, PS1-SAS2 and Stripe 82. This is a best-case scenario. Generally speaking, averaged over the whole sky, PS1 is similar in depth to SDSS in g and r, ~0.5 deeper in i and ~1 mag deeper in z (for point sources).

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See the PS1 DR2 caveats page for more information.

See the PS1 DR1 caveats page for more information.

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The PS1 databases are complex, with information stored in many different tables. The PS1 Sample queries page gives some examples that can be helpful in getting an overview of the use of the tables. The PS1 Database object and detection tables page has detailed descriptions of the tables that have data on individual objects.  You can search within this page for particular columns or quantities. There are other pages that describe tables with observational metadata (e.g., exposure dates and times) and catalog metadata (e.g., information on flags).

If accessing PS1 DR2 data tables through the new, Postgres-backed TAP service, please see the PS1 Migration guide from legacy TAP/CASJobs to TAP on Postgres page for details on the new table structures and how the new tables relate to the legacy PS1 tables.

How can I distinguish stars from galaxies in the catalog?

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The new MAST interface to the Pan-STARRS catalog supports queries to both the DR1 and DR2 PS1 catalogs. It also has an associated API, which is used in the script described here.

How can I

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query and retrieve catalog data using VO TAP?

MAST's Pan-STARRS VO Table Access Protocol (TAP) interfaces allow complex database queries through a SQL-based language, the Astronomical Data Query Language (ADQL).

TAP services are available for a new Postgres-backed version of the PS1 DR2 catalog, as well as access to both the PS1 DR1 and PS1 DR2 catalogs on the legacy database.

Please see the documentation for details on how to retrieve PS1 data using VO TAP. Documentation on the PS1 tables available through these interfaces can be found in the PS1 Migration guide from legacy TAP/CASJobs to TAP on Postgres (Postgres PS1 DR2 service) and PS1 Database object and detection tables (legacy PS1 DR2 and PS1 DR1 services) pages.

How can I search for moving solar system targets in the PS1 images and catalogs?

See the How to search for moving targets in PS1 images and catalogs page for a Python Jupyter notebook that demonstrates one approach to searching the PS1 archive for either images or catalogs that have observations of solar system targets.  A MAST interface is currently under development to make this kind of search easier.

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However, the positions of the mean objects in the DR1 release catalog were recalibrated using Gaia DR1 positions as additional constrains in the astrometric solution.  The Gaia measurements were given very high weight, as detailed in Magnier et al. (2016).  A comparison of Gaia and PanSTARRS positions for the objects in common yields a typical residual of 5 mas (1-sigma, 2-d), with a slightly higher component in the direction of right ascension.  This comparison may not fully reflect PanSTARRS position errors, because of the correlation in the quantities that were compared (Gaia measurements are included in the determination of individual PanSTARRS positions).

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PanSTARRS-Gaia astrometric comparison for sources not used in the PanSTARRS calibration.  Courtesy Makarov et al. (2017).

The majority of the sources in this category have a value of the astrometric_excess_noise parameter in Gaia DR1 below 1 mas (median value 0.61 mas).  The astrometric_excess_noise parameter estimates the magnitude of residuals per observation beyond the nominal measurement errors after the single-object astrometric fit (see Lindegren et al. 2016 for more details).

Makarov et al. (2017) obtain similar results from a comparison with quasars with high-quality radio positions but without a Gaia counterpart, although the number of sources in this case is much smaller.

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PanSTARRS-Quasar astrometric comparison for radio sources without a Gaia counterpart.  Courtesy Makarov et al. (2017).

It is thus likely that systematic uncertainties of 20 mas (1-sigma, 2-d) apply to all mean objects in PanSTARRS DR1, with possibly larger uncertainties in regions with a scarcity of sources in common between Gaia and PanSTARRS. 

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The PS1 camera surveyed the sky using 5 filters: g, r, i, z, and y. The effective wavelengths (and spectral resolutions) of these 5 filters are 481 nm (R = 3.5), 617 nm (R = 4.4), 752 nm (R = 5.8), 866 nm (R = 8.3), and 962 nm (R = 11.6), respectively. Please refer to Table 4 in Tonry et al. (2012) for bandpass details. Schlafly et al. (2012) provides updated zeropoints in Table 1.

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The PS1 photometric system is shown by Schlafly et al. (2012) to have reliability across the survey region at the level of (8.0, 7.0, 9.0, 10.7, 12.4) millimags in (g, r, i, z, y). The Haleakala site is good enough to enable <1% photometry over much of the sky. The PS1 photometric calibration pipeline process is described in Magnier et al. (2016). 

Which magnitudes should I use?

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The times in the warp image headers and in the catalog (e.g., the obsTime column in the Detection table) are defined using international atomic time (TAI) rather than UTC time.  Those times differ by the addition of leap seconds, which leads to header times that differ by 34 or 35 seconds from the UTC times. (See Rots et al. 2015 for more details.) If you are concerned with timing at this level, you may need to convert the times to UTC.  For the warp images, the fix for this is to insert the keyword TIMESYS = 'TAI' in the header.  FITS cutout images have a correct TIMESYS keyword (as of 2022 January 20), but full skycell FITS images do not have a TIMESYS keyword. For the database times, the fix is to subtract 34 or 35 seconds (depending on the date) from the TAI time to get the UTC time.  The astropy.Time module includes convenient functions for converting between TAI and UTC times.

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