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; 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-Lévy; 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

doi:10.1088/0004-6256/150/3/82

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, 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-Lévy, E. BertinD. 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

Institute of Cosmology & Gravitation

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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.

Original language	English
Article number	82
Journal	The Astronomical Journal
Volume	150
Issue number	3
Early online date	20 Aug 2015
DOIs	https://doi.org/10.1088/0004-6256/150/3/82
Publication status	Published - Sept 2015

Keywords

astro-ph.IM
supernovae: general
methods: statistical
methods: data analysis
RCUK
STFC

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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., Papadopoulos, A., Sako, M., Smith, M., Sullivan, M., Thomas, R. C., Wester, W., Wolf, R. C., Abdalla, F. B., Banerji, M., Benoit-Lévy, A., ... Walker, A. R. (2015). Automated transient identification in the Dark Energy Survey. The Astronomical Journal, 150(3), Article 82. https://doi.org/10.1088/0004-6256/150/3/82

@article{f91032edbf4441669f27d4da3a2e1279,

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 (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.",

keywords = "astro-ph.IM, supernovae: general, methods: statistical, methods: data analysis, RCUK, STFC",

author = "Goldstein, {D. A.} and D'Andrea, {C. B.} and Fischer, {J. A.} and Foley, {R. J.} and Gupta, {R. R.} and R. Kessler and Kim, {A. G.} and Nichol, {R. C.} and P. Nugent and A. Papadopoulos and M. Sako and M. Smith and M. Sullivan and Thomas, {R. C.} and W. Wester and Wolf, {R. C.} and Abdalla, {F. B.} and M. Banerji and A. Benoit-L{\'e}vy and E. Bertin and D. Brooks and Rosell, {A. Carnero} and Castander, {F. J.} and Costa, {L. N. da} and R. Covarrubias and DePoy, {D. L.} and S. Desai and Diehl, {H. T.} and P. Doel and Eifler, {T. F.} and Neto, {A. Fausti} and Finley, {D. A.} and B. Flaugher and P. Fosalba and J. Frieman and D. Gerdes and D. Gruen and Gruendl, {R. A.} and D. James and K. Kuehn and N. Kuropatkin and O. Lahav and Li, {T. S.} and Maia, {M. A. G.} and M. Makler and M. March and Marshall, {J. L.} and P. Martini and Merritt, {K. W.} and R. Miquel and B. Nord and R. Ogando and Plazas, {A. A.} and Romer, {A. K.} and A. Roodman and E. Sanchez and V. Scarpine and M. Schubnell and I. Sevilla-Noarbe and Smith, {R. C.} and M. Soares-Santos and F. Sobreira and E. Suchyta and Swanson, {M. E. C.} and G. Tarle and J. Thaler and Walker, {A. R.}",

note = "24 pages, 9 figures, 4 tables v2: updated affiliations, updated fig 4; results unchanged",

year = "2015",

month = sep,

doi = "10.1088/0004-6256/150/3/82",

language = "English",

volume = "150",

journal = "The Astronomical Journal",

issn = "0004-6256",

publisher = "IOP Publishing",

number = "3",

}

Goldstein, DA, D'Andrea, CB, Fischer, JA, Foley, RJ, Gupta, RR, Kessler, R, Kim, AG, Nichol, RC, Nugent, P, Papadopoulos, A, Sako, M, Smith, M, Sullivan, M, Thomas, RC, Wester, W, Wolf, RC, Abdalla, FB, Banerji, M, Benoit-Lévy, A, Bertin, E, Brooks, D, Rosell, AC, Castander, FJ, Costa, LND, Covarrubias, R, DePoy, DL, Desai, S, Diehl, HT, Doel, P, Eifler, TF, Neto, AF, Finley, DA, Flaugher, B, Fosalba, P, Frieman, J, Gerdes, D, Gruen, D, Gruendl, RA, James, D, Kuehn, K, Kuropatkin, N, Lahav, O, Li, TS, Maia, MAG, Makler, M, March, M, Marshall, JL, Martini, P, Merritt, KW, Miquel, R, Nord, B, Ogando, R, Plazas, AA, Romer, AK, Roodman, A, Sanchez, E, Scarpine, V, Schubnell, M, Sevilla-Noarbe, I, Smith, RC, Soares-Santos, M, Sobreira, F, Suchyta, E, Swanson, MEC, Tarle, G, Thaler, J & Walker, AR 2015, 'Automated transient identification in the Dark Energy Survey', The Astronomical Journal, vol. 150, no. 3, 82. https://doi.org/10.1088/0004-6256/150/3/82

TY - JOUR

T1 - Automated transient identification in the Dark Energy Survey

AU - Goldstein, D. A.

AU - D'Andrea, C. B.

AU - Fischer, J. A.

AU - Foley, R. J.

AU - Gupta, R. R.

AU - Kessler, R.

AU - Kim, A. G.

AU - Nichol, R. C.

AU - Nugent, P.

AU - Papadopoulos, A.

AU - Sako, M.

AU - Smith, M.

AU - Sullivan, M.

AU - Thomas, R. C.

AU - Wester, W.

AU - Wolf, R. C.

AU - Abdalla, F. B.

AU - Banerji, M.

AU - Benoit-Lévy, A.

AU - Bertin, E.

AU - Brooks, D.

AU - Rosell, A. Carnero

AU - Castander, F. J.

AU - Costa, L. N. da

AU - Covarrubias, R.

AU - DePoy, D. L.

AU - Desai, S.

AU - Diehl, H. T.

AU - Doel, P.

AU - Eifler, T. F.

AU - Neto, A. Fausti

AU - Finley, D. A.

AU - Flaugher, B.

AU - Fosalba, P.

AU - Frieman, J.

AU - Gerdes, D.

AU - Gruen, D.

AU - Gruendl, R. A.

AU - James, D.

AU - Kuehn, K.

AU - Kuropatkin, N.

AU - Lahav, O.

AU - Li, T. S.

AU - Maia, M. A. G.

AU - Makler, M.

AU - March, M.

AU - Marshall, J. L.

AU - Martini, P.

AU - Merritt, K. W.

AU - Miquel, R.

AU - Nord, B.

AU - Ogando, R.

AU - Plazas, A. A.

AU - Romer, A. K.

AU - Roodman, A.

AU - Sanchez, E.

AU - Scarpine, V.

AU - Schubnell, M.

AU - Sevilla-Noarbe, I.

AU - Smith, R. C.

AU - Soares-Santos, M.

AU - Sobreira, F.

AU - Suchyta, E.

AU - Swanson, M. E. C.

AU - Tarle, G.

AU - Thaler, J.

AU - Walker, A. R.

N1 - 24 pages, 9 figures, 4 tables v2: updated affiliations, updated fig 4; results unchanged

PY - 2015/9

Y1 - 2015/9

N2 - 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.

AB - 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.

KW - astro-ph.IM

KW - supernovae: general

KW - methods: statistical

KW - methods: data analysis

KW - RCUK

KW - STFC

U2 - 10.1088/0004-6256/150/3/82

DO - 10.1088/0004-6256/150/3/82

M3 - Article

SN - 0004-6256

VL - 150

JO - The Astronomical Journal

JF - The Astronomical Journal

IS - 3

M1 - 82

ER -

Automated transient identification in the Dark Energy Survey

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Keywords

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