Pith. sign in

REVIEW

Automated Transient Identification in the Dark Energy Survey

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1504.02936 v3 pith:YXWBSRZ4 submitted 2015-04-12 astro-ph.IM

Automated Transient Identification in the Dark Energy Survey

D. A. Goldstein , C. B. D'Andrea , J. A. Fischer , R. J. Foley , R. R. Gupta , R. Kessler , A. G. Kim , R. C. Nichol
show 59 more authors
P. Nugent A. Papadopoulos M. Sako M. Smith M. Sullivan R. C. Thomas W. Wester R. C. Wolf F. B. Abdalla M. Banerji A. Benoit-L\'evy E. Bertin D. Brooks A. Carnero Rosell F. J. Castander L. N. da Costa R. Covarrubias D. L. DePoy S. Desai H. T. Diehl P. Doel T. F. Eifler A. Fausti Neto D. A. Finley B. Flaugher P. Fosalba J. Frieman D. Gerdes D. Gruen R. A. Gruendl D. James K. Kuehn N. Kuropatkin O. Lahav T. S. Li M. A. G. Maia M. Makler M. March J. L. Marshall P. Martini K. W. Merritt R. Miquel B. Nord R. Ogando A. A. Plazas A. K. Romer A. Roodman E. Sanchez V. Scarpine M. Schubnell I. Sevilla-Noarbe R. C. Smith M. Soares-Santos F. Sobreira E. Suchyta M. E. C. Swanson G. Tarle J. Thaler A. R. Walker
This is my paper
classification astro-ph.IM
keywords algorithmsurveytransientdarkdatades-snenergyevents
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original 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 (Sep. 2013 through Feb. 2014) using the algorithm, the number of transient candidates eligible for human scanning decreased by a factor of 13.4, while only 1 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. An implementation of the algorithm and the training data used in this paper are available at http://portal.nersc.gov/project/dessn/autoscan.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.