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A Morphological Classification Model to Identify Unresolved PanSTARRS1 Sources: Application in the ZTF Real-time Pipeline

Tachibana, Yutaro and Miller, A. A. (2018) A Morphological Classification Model to Identify Unresolved PanSTARRS1 Sources: Application in the ZTF Real-time Pipeline. Publications of the Astronomical Society of the Pacific, 130 (994). Art. No. 128001. ISSN 0004-6280.

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In the era of large photometric surveys, the importance of automated and accurate classification is rapidly increasing. Specifically, the separation of resolved and unresolved sources in astronomical imaging is a critical initial step for a wide array of studies, ranging from Galactic science to large scale structure and cosmology. Here, we present our method to construct a large, deep catalog of point sources utilizing Pan-STARRS1 (PS1) 3π survey data, which consists of ~3 × 10^9 sources with m ≾ 23.5 mag. We develop a supervised machine-learning methodology, using the random forest (RF) algorithm, to construct the PS1 morphology model. We train the model using ~5 × 10^4 PS1 sources with HST COSMOS morphological classifications and assess its performance using ~4 × 10^6 sources with Sloan Digital Sky Survey (SDSS) spectra and ~2 × 108^ Gaia sources. We construct 11 "white flux" features, which combine PS1 flux and shape measurements across five filters, to increase the signal-to-noise ratio relative to any individual filter. The RF model is compared to three alternative models, including the SDSS and PS1 photometric classification models, and we find that the RF model performs best. By number the PS1 catalog is dominated by faint sources (m ≳ 21 mag), and in this regime the RF model significantly outperforms the SDSS and PS1 models. For time-domain surveys, identifying unresolved sources is crucial for inferring the Galactic or extragalactic origin of new transients. We have classified ~1.5 × 10^9 sources using the RF model, and these results are used within the Zwicky Transient Facility real-time pipeline to automatically reject stellar sources from the extragalactic alert stream.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Tachibana, Yutaro0000-0001-6584-6945
Miller, A. A.0000-0001-9515-478X
Additional Information:© 2018. The Astronomical Society of the Pacific. Received 2018 August 23; accepted 2018 September 20; published 2018 November 12. Y.T. is funded by JSPS KAKENHI grant Nos. JP16J05742. Y.T. studied as a Global Relay of Observatories Watching Transients Happen (GROWTH) intern at Caltech during the summer and fall of 2017. GROWTH is funded by the National Science Foundation under Partnerships for International Research and Education grant No. 1545949. A.A.M. is funded by the Large Synoptic Survey Telescope Corporation in support of the Data Science Fellowship Program. Based in part on software developed as a part of the Zwicky Transient Facility project. Major funding has been provided by the U.S National Science Foundation under grant No. AST-1440341 and by the ZTF partner institutions: the California Institute of Technology, the Oskar Klein Centre, the Weizmann Institute of Science, the University of Maryland, the University of Washington, Deutsches Elektronen-Synchrotron, the University of Wisconsin-Milwaukee, and the TANGO Program of the University System of Taiwan. Facilities: PS1 - , Sloan. - Software: astropy (Astropy Collaboration et al. 2013), scipy (Jones et al. 2001), matplotlib (Hunter 2007), pandas (McKinney 2010), scikit-learn (Pedregosa et al. 2012).
Group:Zwicky Transient Facility
Funding AgencyGrant Number
Japan Society for the Promotion of Science (JSPS)JP16J05742
Large Synoptic Survey Telescope CorporationUNSPECIFIED
ZTF partner institutionsUNSPECIFIED
Subject Keywords:catalogs – methods: data analysis – methods: statistical
Issue or Number:994
Record Number:CaltechAUTHORS:20181113-112609672
Persistent URL:
Official Citation:Yutaro Tachibana and A. A. Miller 2018 PASP 130 128001
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:90868
Deposited By: George Porter
Deposited On:13 Nov 2018 23:59
Last Modified:03 Oct 2019 20:29

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