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Title: Automated transient identification in the Dark Energy Survey

Abstract

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 percent 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.

Authors:
 [1]
  1. Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). et al.
Publication Date:
Research Org.:
Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP)
Contributing Org.:
DES
OSTI Identifier:
1212752
Report Number(s):
FERMILAB-PUB-15-141-AE
Journal ID: ISSN 1538-3881; arXiv eprint number arXiv:1504.02936
DOE Contract Number:  
AC02-07CH11359
Resource Type:
Journal Article
Journal Name:
The Astronomical Journal (Online)
Additional Journal Information:
Journal Volume: 150; Journal Issue: 3; Journal ID: ISSN 1538-3881
Country of Publication:
United States
Language:
English
Subject:
transients; discovery; algorithms; statistical; random forest; machine learning

Citation Formats

Goldstein, D. A. Automated transient identification in the Dark Energy Survey. United States: N. p., 2015. Web. doi:10.1088/0004-6256/150/3/82.
Goldstein, D. A. Automated transient identification in the Dark Energy Survey. United States. https://doi.org/10.1088/0004-6256/150/3/82
Goldstein, D. A. 2015. "Automated transient identification in the Dark Energy Survey". United States. https://doi.org/10.1088/0004-6256/150/3/82. https://www.osti.gov/servlets/purl/1212752.
@article{osti_1212752,
title = {Automated transient identification in the Dark Energy Survey},
author = {Goldstein, D. A.},
abstractNote = {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 percent 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.},
doi = {10.1088/0004-6256/150/3/82},
url = {https://www.osti.gov/biblio/1212752}, journal = {The Astronomical Journal (Online)},
issn = {1538-3881},
number = 3,
volume = 150,
place = {United States},
year = {2015},
month = {8}
}

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Works referencing / citing this record:

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Cosmological Constraints from Multiple Probes in the Dark Energy Survey
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Optical Transient Object Classification in Wide-field Small Aperture Telescopes with a Neural Network
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Automated Transient Detection with Shapelet Analysis in Image-subtracted Data
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