2022.06.30: PS1 astrometry updated using Gaia EDR3
The astrometry in PS1 has been updated using Gaia EDR3. Systematic astrometric distortions and color terms (from differential chromatic aberration) are greatly reduced. It is still possible to access the old positions, but the new astrometry is preferred for most purposes. See PS1 Astrometry Correction Using Gaia EDR3 for more details.
2021.11.04: Bulk image download Python script
2020.06.29: PS1 VO Table Access Protocol information added
PS1 has a Virtual Observatory-compatible Table Access Protocol (TAP) interface for the PS1 database. It provides an alternative to CasJobs for SQL queries of the database. See the How to retrieve and use PS1 data page for additional information, including a link to a Python Jupyter notebook.
2020.06.29: Missing data sky regions updated
The PS1 DR2 caveats page's description of sky regions with missing data in the PS1 database has been updated. Currently about 4.9 square degrees (only 0.016%) of the sky has missing objects. That is substantially reduced from the missing area at the time of the DR2 release.
2019.12.11: Easy cross-match using the MAST catalogs API
A new web page, Easy cross-match with PS1 using a list of source positions, shows that it is very simple to cross-match a list of positions to the PS1 catalog using a single curl command or a simple Python script.
2019.02.25: ForcedMeanObject and ForcedMeanLensing tables available
The new database replaces the old one since it is preferred for all purposes. Existing objects in the database did not change in any way. This version of the database is thought to be complete over the sky.
2017.02.02: DR1 database now has 1.
5 percent more objects
A new version of the CasJobs PanSTARRS_DR1 database has been installed. This adds about 1.5% of objects that were missing in the 2016 Dec 19 release of ObjectThin, MeanObject, and StackObjectThin tables. For users who have done large-scale queries and want to get just the new objects, there are 3 new tables to make this easier. ObjectThinMissing has 132 million objects that were previously missing from ObjectThin; MeanObjectMissing has the same 132 million objects that were missing from MeanObject; and StackObjectThinMissing has 40 million objects that were missing from StackObjectThin. It should be possible to run most queries again using these much smaller tables to quickly fill in missing data.