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. E. 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. E. 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

Research output: Contribution to journal › Article › Research › peer-review

76 Citations (Scopus)

Abstract

© 2015. The American Astronomical Society. All rights reserved.. 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.

Original language	English
Article number	82
Journal	Astronomical Journal
Volume	150
Issue number	3
DOIs	https://doi.org/10.1088/0004-6256/150/3/82
Publication status	Published - 1 Sep 2015

Keywords

methods: data analysis
methods: statistical
supernovae: general

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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. E., 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. Astronomical Journal, 150(3), [82]. https://doi.org/10.1088/0004-6256/150/3/82

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-Lévy, A. ; Bertin, E. ; Brooks, D. ; Rosell, A. Carnero ; Castander, F. J. ; Costa, L. N.Da ; Covarrubias, R. ; Depoy, D. L. ; Desai, S. ; Diehl, H. T. ; Doel, P. ; Eifler, T. F. ; Neto, A. Fausti ; 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. ; Walker, A. R. / AUTOMATED TRANSIENT IDENTIFICATION in the DARK ENERGY SURVEY. In: Astronomical Journal. 2015 ; Vol. 150, No. 3.

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title = "AUTOMATED TRANSIENT IDENTIFICATION in the DARK ENERGY SURVEY",

abstract = "{\textcopyright} 2015. The American Astronomical Society. All rights reserved.. 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.",

keywords = "methods: data analysis, methods: statistical, supernovae: general",

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 Nugent, {P. E.} 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.}",

year = "2015",

month = sep,

day = "1",

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

language = "English",

volume = "150",

journal = "Astronomical Journal",

issn = "0004-6256",

number = "3",

}

Goldstein, DA, D'Andrea, CB, Fischer, JA, Foley, RJ, Gupta, RR, Kessler, R, Kim, AG, Nichol, RC, Nugent, PE, 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', Astronomical Journal, vol. 150, no. 3, 82. https://doi.org/10.1088/0004-6256/150/3/82

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-Lévy, A.; Bertin, E.; Brooks, D.; Rosell, A. Carnero; Castander, F. J.; Costa, L. N.Da; Covarrubias, R.; Depoy, D. L.; Desai, S.; Diehl, H. T.; Doel, P.; Eifler, T. F.; Neto, A. Fausti; 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.; Walker, A. R.

In: Astronomical Journal, Vol. 150, No. 3, 82, 01.09.2015.

Research output: Contribution to journal › Article › Research › peer-review

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. E.

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.

PY - 2015/9/1

Y1 - 2015/9/1

N2 - © 2015. The American Astronomical Society. All rights reserved.. 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.

AB - © 2015. The American Astronomical Society. All rights reserved.. 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.

KW - methods: data analysis

KW - methods: statistical

KW - supernovae: general

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

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

M3 - Article

VL - 150

JO - Astronomical Journal

JF - Astronomical Journal

SN - 0004-6256

IS - 3

M1 - 82

ER -