CaltechAUTHORS
  A Caltech Library Service

Automating Discovery and Classification of Transients and Variable Stars in the Synoptic Survey Era

Bloom, J. S. and Richards, J. W. and Nugent, P. E. and Quimby, R. M. and Kasliwal, M. M. and Starr, D. L. and Poznanski, D. and Ofek, E. O. and Cenko, S. B. and Butler, N. R. and Kulkarni, S. R. and Gal-Yam, A. and Law, N. (2012) Automating Discovery and Classification of Transients and Variable Stars in the Synoptic Survey Era. Publications of the Astronomical Society of the Pacific, 124 (921). pp. 1175-1196. ISSN 0004-6280. http://resolver.caltech.edu/CaltechAUTHORS:20121207-095356939

[img]
Preview
PDF - Published Version
See Usage Policy.

1964Kb

Use this Persistent URL to link to this item: http://resolver.caltech.edu/CaltechAUTHORS:20121207-095356939

Abstract

The rate of image acquisition in modern synoptic imaging surveys has already begun to outpace the feasibility of keeping astronomers in the real-time discovery and classification loop. Here we present the inner workings of a framework, based on machine-learning algorithms, that captures expert training and ground-truth knowledge about the variable and transient sky to automate (1) the process of discovery on image differences, and (2) the generation of preliminary science-type classifications of discovered sources. Since follow-up resources for extracting novel science from fast-changing transients are precious, self-calibrating classification probabilities must be couched in terms of efficiencies for discovery and purity of the samples generated. We estimate the purity and efficiency in identifying real sources with a two-epoch image-difference discovery algorithm for the Palomar Transient Factory (PTF) survey. Once given a source discovery, using machine-learned classification trained on PTF data, we distinguish between transients and variable stars with a 3.8% overall error rate (with 1.7% errors for imaging within the Sloan Digital Sky Survey footprint). At >96% classification efficiency, the samples achieve 90% purity. Initial classifications are shown to rely primarily on context-based features, determined from the data itself and external archival databases. In the first year of autonomous operations of PTF, this discovery and classification framework led to several significant science results, from outbursting young stars to subluminous Type IIP supernovae to candidate tidal disruption events. We discuss future directions of this approach, including the possible roles of crowdsourcing and the scalability of machine learning to future surveys such as the Large Synoptic Survey Telescope (LSST).


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1086/668468DOIArticle
http://www.jstor.org/stable/10.1086/668468JSTORArticle
ORCID:
AuthorORCID
Nugent, P. E.0000-0002-3389-0586
Quimby, R. M.0000-0001-9171-5236
Kasliwal, M. M.0000-0002-5619-4938
Ofek, E. O.0000-0002-6786-8774
Cenko, S. B.0000-0003-1673-970X
Kulkarni, S. R.0000-0001-5390-8563
Gal-Yam, A.0000-0002-3653-5598
Law, N.0000-0001-9380-6457
Additional Information:© 2012 Astronomical Society of the Pacific. Received 2011 June 27; accepted 2012 September 12; published 2012 October 29. The authors acknowledge the generous support of a CDI grant (#0941742) from the National Science Foundation. J.S.B. and D.L.S. also thank the Las Cumbres Observatory for support during the early stages of this work. S.B.C. wishes to acknowledge generous support from Gary and Cynthia Bengier, the Richard and Rhoda Goldman Fund, National Aeronautics and Space Administration (NASA)/Swift grant NNX10AI21G, NASA/Fermi grant NNX1OA057G, and National Science Foundation (NSF) grant AST-0908886. The National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract #DE-AC02-05CH11231, provided staff, computational resources, and data storage for this project. Some of the data presented herein were obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California, and the National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the W. M. Keck Foundation. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Mauna Kea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain. This research has made use of the VizieR catalogue access tool, CDS, Strasbourg, France (Ochsenbein et al. 2000).
Group:IPTF
Funders:
Funding AgencyGrant Number
NSFCDI-0941742
Las Cumbres ObservatoryUNSPECIFIED
Gary and Cynthia BengierUNSPECIFIED
Richard and Rhoda Goldman FundUNSPECIFIED
NASANNX10AI21G
NASANNX1OA057G
NSFAST-0908886
Subject Keywords:Data Analysis and Techniques
Record Number:CaltechAUTHORS:20121207-095356939
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20121207-095356939
Official Citation:Automating Discovery and Classification of Transients and Variable Stars in the Synoptic Survey Era J. S. Bloom, J. W. Richards, P. E. Nugent, R. M. Quimby, M. M. Kasliwal, D. L. Starr, D. Poznanski, E. O. Ofek, S. B. Cenko, N. R. Butler, S. R. Kulkarni, A. Gal-Yam and N. Law Publications of the Astronomical Society of the Pacific , Vol. 124, No. 921 (November 2012), pp. 1175-1196 Published by: The University of Chicago Press on behalf of the Astronomical Society of the Pacific Article DOI: 10.1086/668468 Article Stable URL: http://www.jstor.org/stable/10.1086/668468
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:35872
Collection:CaltechAUTHORS
Deposited By: Jason Perez
Deposited On:07 Dec 2012 22:31
Last Modified:27 Oct 2017 03:22

Repository Staff Only: item control page