A transient search using combined human and machine classifications

Darryl E. Wright, Chris J. Lintott, Stephen J. Smartt, Ken W. Smith, Lucy Fortson, Laura Trouille, Campbell R. Allen, Melanie Beck, Mark C. Bouslog, Amy Boyer, K. C. Chambers, Heather Flewelling, Will Granger, Eugene A. Magnier, Adam McMaster, Grant R. M. Miller, James E. O'Donnell, Helen Spiers, John L. Tonry, Marten VeldthuisRichard J. Wainscoat, Chris Waters, Mark Willman, Zach Wolfenbarger, Dave R. Young

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


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 LSST and other large-throughput surveys.
Original languageUndefined/Unknown
JournalMonthly Notices of the Royal Astronomical Society
StatePublished - Jul 17 2017

Bibliographical note

10 pages, 9 figures, submitted to MNRAS


  • astro-ph.IM

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