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A Morphological Classification Model to Identify Unresolved PanSTARRS1 Sources. II. Update to the PS1 Point Source Catalog

Miller, A. A. and Hall, X. J. (2021) A Morphological Classification Model to Identify Unresolved PanSTARRS1 Sources. II. Update to the PS1 Point Source Catalog. Publications of the Astronomical Society of the Pacific, 133 (1023). Art. No. 054502. ISSN 0004-6280. doi:10.1088/1538-3873/abf038. https://resolver.caltech.edu/CaltechAUTHORS:20210512-131445501

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Abstract

We present an update to the PanSTARRS-1 Point Source Catalog (PS1 PSC), which provides morphological classifications of PS1 sources. The original PS1 PSC adopted stringent detection criteria that excluded hundreds of millions of PS1 sources from the PSC. Here, we adapt the supervised machine learning methods used to create the PS1 PSC and apply them to different photometric measurements that are more widely available, allowing us to add ~144 million new classifications while expanding the the total number of sources in PS1 PSC by ~10%. We find that the new methodology, which utilizes PS1 forced photometry, performs ~6%–8% worse than the original method. This slight degradation in performance is offset by the overall increase in the size of the catalog. The PS1 PSC is used by time-domain surveys to filter transient alert streams by removing candidates coincident with point sources that are likely to be Galactic in origin. The addition of ~144 million new classifications to the PS1 PSC will improve the efficiency with which transients are discovered.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1088/1538-3873/abf038DOIArticle
https://arxiv.org/abs/2012.01544arXivDiscussion Paper
ORCID:
AuthorORCID
Miller, A. A.0000-0001-9515-478X
Hall, X. J.0000-0002-9364-5419
Additional Information:© 2021. The Astronomical Society of the Pacific. Received 2021 January 20; accepted 2021 March 8; published 2021 April 29. This work would not have been possible without the public release of the PS1 data. We thank F. Masci and R. Laher for helping us identify sources that were not classified in the ZTF Stars table. We thank the anonymous referee for comments that improved this manuscript. A.A.M. is funded by the Large Synoptic Survey Telescope Corporation (LSSTC), the Brinson Foundation, and the Moore Foundation in support of the LSSTC Data Science Fellowship Program; he also receives support as a CIERA Fellow by the CIERA Postdoctoral Fellowship Program (Center for Interdisciplinary Exploration and Research in Astrophysics, Northwestern University). X.J.H. is supported by LSSTC, through Enabling Science Grant #2020-01. Facility: PS1 (Chambers et al. 2016). Software: astropy (Astropy Collaboration et al. 2013, 2018), scipy (Virtanen et al. 2020), matplotlib (Hunter 2007), pandas (McKinney 2010), scikit-learn (Pedregosa et al. 2011).
Funders:
Funding AgencyGrant Number
Large Synoptic Survey Telescope Corporation2020-01
Brinson FoundationUNSPECIFIED
Gordon and Betty Moore FoundationUNSPECIFIED
Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA)UNSPECIFIED
Subject Keywords:Catalogs – Computational methods – Astrostatistics techniques
Issue or Number:1023
DOI:10.1088/1538-3873/abf038
Record Number:CaltechAUTHORS:20210512-131445501
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210512-131445501
Official Citation:A. A. Miller and X. J. Hall 2021 PASP 133 054502
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109104
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:12 May 2021 20:39
Last Modified:12 May 2021 20:39

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