10-1088-0004-6256-150-3-82.pdf.pdf (1.96 MB)
Automated transient identification in the Dark Energy Survey
journal contribution
posted on 2023-06-09, 01:55 authored by Kathy RomerKathy Romer, et al The DES CollaborationWe 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.
Funding
Astrophysics and Cosmology - Sussex Consolidated Grant; G1291; STFC-SCIENCE AND TECHNOLOGY FACILITIES COUNCIL; ST/L000652/1
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- Published
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- Published version
Journal
Astronomical JournalISSN
0004-6256Publisher
American Astronomical SocietyExternal DOI
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3Volume
150Page range
82Department affiliated with
- Physics and Astronomy Publications
Notes
An erratum for this article has been published in 2015 The Astronomical Journal 150 165Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2016-06-27First Open Access (FOA) Date
2016-06-27First Compliant Deposit (FCD) Date
2016-06-27Usage metrics
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