ERRATUM: “Automated Transient Identification in the Dark Energy Survey” (2015, AJ, 150, 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.

doi:10.1088/0004-6256/150/5/165

Title: ERRATUM: “Automated Transient Identification in the Dark Energy Survey” (2015, AJ, 150, 82)

Full Record
Other Related Research

Abstract

Here, 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 at http://portal.nersc.gov/project/dessn/autoscan.

Authors:

; D’Andrea, C. B.; Fischer, J. A.; Foley, R. J.; Gupta, R. R.;

; Kim, A. G.; Nichol, R. C.; Nugent, P. E.; Papadopoulos, A.; Sako, M.; Smith, M.;

; Thomas, R. C.; Wester, W.; Wolf, R. C.; Abdalla, F. B.; Banerji, M.; Benoit-Lévy, A.; Bertin, E. more »

Publication Date:: Thu Aug 20 00:00:00 EDT 2015

Research Org.:: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Sponsoring Org.:: USDOE Office of Science (SC), High Energy Physics (HEP)

Contributing Org.:: DES Collaboration

OSTI Identifier:: 1456916

Grant/Contract Number:: AC02-05CH11231

Resource Type:: Accepted Manuscript

Journal Name:: Astronomical Journal (Online)

Additional Journal Information:: Journal Name: Astronomical Journal (Online); Journal Volume: 150; Journal Issue: 5; Journal ID: ISSN 1538-3881

Publisher:: IOP Publishing - AAAS

Country of Publication:: United States

Language:: English

Subject:: 79 ASTRONOMY AND ASTROPHYSICS; transients-discovery; algorithms-statistical; random forest; machine learning

Citation Formats


                    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., and Walker, A. R. ERRATUM: “Automated Transient Identification in the Dark Energy Survey” (2015, AJ, 150, 82).  United States: N. p., 2015. 
Web.  doi:10.1088/0004-6256/150/5/165.

Copy to clipboard


                    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. ERRATUM: “Automated Transient Identification in the Dark Energy Survey” (2015, AJ, 150, 82).  United States.  https://doi.org/10.1088/0004-6256/150/5/165

Copy to clipboard


                    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., and Walker, A. R. Thu .  
"ERRATUM: “Automated Transient Identification in the Dark Energy Survey” (2015, AJ, 150, 82)".  United States.  https://doi.org/10.1088/0004-6256/150/5/165.  https://www.osti.gov/servlets/purl/1456916.

Copy to clipboard


                    
@article{osti_1456916,

  title        = {ERRATUM: “Automated Transient Identification in the Dark Energy Survey” (2015, AJ, 150, 82)},

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

  abstractNote = {Here, 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 at http://portal.nersc.gov/project/dessn/autoscan.},

  doi          = {10.1088/0004-6256/150/5/165},

  journal      = {Astronomical Journal (Online)},

  number       = 5,

  volume       = 150,

  place        = {United States},

  year         = {Thu Aug 20 00:00:00 EDT 2015},

  month        = {Thu Aug 20 00:00:00 EDT 2015}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (DOE)

Publisher's Version of Record

https://doi.org/10.1088/0004-6256/150/5/165

Other availability

Search WorldCat to find libraries that may hold this journal

Citation Metrics:

Cited by: 2 works

Citation information provided by
Web of Science

Save / Share:

Export Metadata

Save to My Library

Similar Records in DOE PAGES and OSTI.GOV collections:

AUTOMATED TRANSIENT IDENTIFICATION IN THE DARK ENERGY SURVEY

Journal Article Goldstein, D. A. ; Nugent, P. E. ; D’Andrea, C. B. ; ... - Astronomical Journal (Online)

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 factormore »« less
https://doi.org/10.1088/0004-6256/150/3/82
AUTOMATED TRANSIENT IDENTIFICATION IN THE DARK ENERGY SURVEY

Journal Article Goldstein, D. A. ; D’Andrea, C. B. ; Fischer, J. A. ; ... - The Astronomical Journal (Online)

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 factormore »« less
https://doi.org/10.1088/0004-6256/150/3/82
Automated transient identification in the Dark Energy Survey

Journal Article Goldstein, D. A. ; D'Andrea, C. B. ; Fischer, J. A. ; ... - The Astronomical Journal (Online)

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 factormore »« less
Cited by 105
https://doi.org/10.1088/0004-6256/150/3/82

Full Text Available
Automated transient identification in the Dark Energy Survey

Journal Article Goldstein, D. A. - The Astronomical Journal (Online)

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 factormore »« less
https://doi.org/10.1088/0004-6256/150/3/82

Full Text Available
How to Find Gravitationally Lensed Type Ia supernovae

Journal Article Goldstein, Daniel A. ; Nugent, Peter E. - The Astrophysical Journal. Letters (Online)

Type Ia supernovae (SNe Ia) that are multiply imaged by gravitational lensing can extend the SN Ia Hubble diagram to very high redshifts (z ≳ 2), probe potential SN Ia evolution, and deliver high-precision constraints on H₀, w, and Ω_m via time delays. However, only one, iPTF16geu, has been found to date, and many more are needed to achieve these goals. To increase the multiply imaged SN Ia discovery rate, we present a simple algorithm for identifying gravitationally lensed SN Ia candidates in cadenced, wide-field optical imaging surveys. The technique is to look for supernovae that appear to be hostedmore »« less
Cited by 45
https://doi.org/10.3847/2041-8213/834/1/L5

Full Text Available

Similar Records