2015AJ....150...82G

Query : 2015AJ....150...82G

2015AJ....150...82G - Astron. J., 150, 82 (2015/September-0)

Automated transient identification in the Dark Energy Survey.

GOLDSTEIN D.A., D'ANDREA C.B., FISCHER J.A., FOLEY R.J., GUPTA R.R., KESSLER R., KIM A.G., NICHOL R.C., NUGENT P.E., PAPADOPOULOS A., SAKO M., SMITH M., SULLIVAN M., THOMAS R.C., WESTER W., WOLF R.C., ABDALLA F.B., BANERJI M., BENOIT-LEVY A., BERTIN E., BROOKS D., CARNERO ROSELL A., CASTANDER F.J., DA COSTA L.N., COVARRUBIAS R., DEPOY D.L., DESAI S., DIEHL H.T., DOEL P., EIFLER T.F., FAUSTI NETO A., FINLEY D.A., FLAUGHER B., FOSALBA P., FRIEMAN J., GERDES D., GRUEN D., GRUENDL R.A., JAMES D., KUEHN K., KUROPATKIN N., LAHAV O., LI T.S., MAIA M.A.G., MAKLER M., MARCH M., MARSHALL J.L., MARTINI P., MERRITT K.W., MIQUEL R., NORD B., OGANDO R., PLAZAS A.A., ROMER A.K., ROODMAN A., SANCHEZ E., SCARPINE V., SCHUBNELL M., SEVILLA-NOARBE I., SMITH R.C., SOARES-SANTOS M., SOBREIRA F., SUCHYTA E., SWANSON M.E.C., TARLE G., THALER J. and WALKER A.R.

Abstract (from CDS):

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 http://portal.nserc.gov/project/dessn/autoscan.

Abstract Copyright: ∼

Journal keyword(s): methods: data analysis - methods: statistical - supernovae: general

Errata: erratum vol. 150, art. 165 (2015)

Simbad objects: 1

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Number of rows : 1
N Identifier Otype ICRS (J2000)
RA 		ICRS (J2000)
DEC 		Mag U Mag B Mag V Mag R Mag I Sp type #ref
1850 - 2026 		#notes
1 NAME Chandra Deep Field-South reg 03 32 28.0 -27 48 30           ~ 2282 1

Query : 2015AJ....150...82G

Basic data :
NAME Chandra Deep Field-South -- Region defined in the Sky
Other object types: otypes_help 		reg ()
ICRS coord. (ep=J2000) : 		03 32 28.0 -27 48 30 [ ] D 2001ApJ...551..624G
FK4 coord. (ep=B1950 eq=1950) : 		03 30 22.4 -27 58 35 [ ]
Gal coord. (ep=J2000) : 		223.5732 -54.4374 [ ]
Angular size (arcmin): 		15.84 15.84 ~ (~) D ~
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NAME Chandra Deep Field-South NAME CDFS NAME CDF-S NAME Chandra Deep Field South

References (2282 between 1850 and 2026) (Total 2282)
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