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Title: AUTOMATED TRANSIENT IDENTIFICATION IN THE DARK ENERGY SURVEY

We describe an algorithm for identifying point-source transients and moving objects on reference-subtracted optical images containing artifacts of processing and instrumentation. The algorithm makes use of the supervised machine learning technique known as Random Forest. We present results from its use in the Dark Energy Survey Supernova program (DES-SN), where it was trained using a sample of 898,963 signal and background events generated by the transient detection pipeline. After reprocessing the data collected during the first DES-SN observing season (2013 September through 2014 February) using the algorithm, the number of transient candidates eligible for human scanning decreased by a factor of 13.4, while only 1.0% of the artificial Type Ia supernovae (SNe) injected into search images to monitor survey efficiency were lost, most of which were very faint events. Here we characterize the algorithm’s performance in detail, and we discuss how it can inform pipeline design decisions for future time-domain imaging surveys, such as the Large Synoptic Survey Telescope and the Zwicky Transient Facility. An implementation of the algorithm and the training data used in this paper are available at at http://portal.nersc.gov/project/dessn/autoscan.
Authors:
;  [1] ; ; ;  [2] ; ; ;  [3] ;  [4] ;  [5] ;  [6] ; ;  [7] ; ;  [8] ;  [9] ; ;  [10] ;  [11] ;  [12] more »; « less
  1. Department of Astronomy, University of California, Berkeley, 501 Campbell Hall #3411, Berkeley, CA 94720 (United States)
  2. Institute of Cosmology and Gravitation, University of Portsmouth, Dennis Sciama Building, Burnaby Road, Portsmouth, PO1 3FX (United Kingdom)
  3. Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104 (United States)
  4. Astronomy Department, University of Illinois at Urbana-Champaign, 1002 West Green Street, Urbana, IL 61801 (United States)
  5. Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439 (United States)
  6. Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637 (United States)
  7. Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720 (United States)
  8. School of Physics and Astronomy, University of Southampton, Highfield, Southampton, SO17 1BJ (United Kingdom)
  9. Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510 (United States)
  10. Department of Physics and Astronomy, University College London, Gower Street, London, WC1E 6BT (United Kingdom)
  11. Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge CB3 0HA (United Kingdom)
  12. Institut d’Astrophysique de Paris, Univ. Pierre et Marie Curie and CNRS UMR7095, F-75014 Paris (France)
Publication Date:
OSTI Identifier:
22520031
Resource Type:
Journal Article
Resource Relation:
Journal Name: Astronomical Journal (Online); Journal Volume: 150; Journal Issue: 3; Other Information: Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
79 ASTROPHYSICS, COSMOLOGY AND ASTRONOMY; ALGORITHMS; DATA ANALYSIS; DATA PROCESSING; EFFICIENCY; IMAGES; IMPLEMENTATION; NONLUMINOUS MATTER; POINT SOURCES; RANDOMNESS; REPROCESSING; SUPERNOVAE; TELESCOPES; TRANSIENTS