A transient search using combined human and machine classifications

Wright, Darryl E. and Lintott, Chris J. and Smartt, Stephen J. and Smith, Ken W. and Fortson, Lucy and Trouille, Laura and Allen, Campbell R. and Beck, Melanie and Bouslog, Mark C. and Boyer, Amy and Chambers, K. C. and Flewelling, Heather and Granger, Will and Magnier, Eugene A. and Mcmaster, Adam and Miller, Grant R. M. and O'donnell, James E. and Simmons, Brooke and Spiers, Helen and Tonry, John L. and Veldthuis, Marten and Wainscoat, Richard J. and Waters, Chris and Willman, Mark and Wolfenbarger, Zach and Young, Dave R. (2017) A transient search using combined human and machine classifications. Monthly Notices of the Royal Astronomical Society, 472 (2). pp. 1315-1323. ISSN 0035-8711

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Abstract

Large modern surveys require efficient review of data in order to find transient sources such as supernovae, and to distinguish such sources from artefacts and noise. Much effort has been put into the development of automatic algorithms, but surveys still rely on human review of targets. This paper presents an integrated system for the identification of supernovae in data from Pan-STARRS1, combining classifications from volunteers participating in a citizen science project with those from a convolutional neural network. The unique aspect of this work is the deployment, in combination, of both human and machine classifications for near real-time discovery in an astronomical project. We show that the combination of the two methods outperforms either one used individually. This result has important implications for the future development of transient searches, especially in the era of Large Synoptic Survey Telescope and other large-throughput surveys.

Item Type:
Journal Article
Journal or Publication Title:
Monthly Notices of the Royal Astronomical Society
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1900/1912
Subjects:
ID Code:
127614
Deposited By:
Deposited On:
21 Sep 2018 15:30
Refereed?:
Yes
Published?:
Published
Last Modified:
27 Sep 2020 04:47