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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 (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.

Authors:
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Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1392340
DOE Contract Number:
AC02-06CH11357
Resource Type:
Journal Article
Resource Relation:
Journal Name: Astronomical Journal (Online); Journal Volume: 150; Journal Issue: 3
Country of Publication:
United States
Language:
English

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. AUTOMATED TRANSIENT IDENTIFICATION IN THE DARK ENERGY SURVEY. United States: N. p., 2015. Web. doi: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. United States. doi: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., and Walker, A. R. 2015. "AUTOMATED TRANSIENT IDENTIFICATION IN THE DARK ENERGY SURVEY". United States. doi:10.1088/0004-6256/150/3/82.
@article{osti_1392340,
title = {AUTOMATED TRANSIENT IDENTIFICATION IN THE DARK ENERGY SURVEY},
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 = {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.},
doi = {10.1088/0004-6256/150/3/82},
journal = {Astronomical Journal (Online)},
number = 3,
volume = 150,
place = {United States},
year = 2015,
month = 8
}
  • 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 » of 13.4, while only 1.0 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.« less
  • 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 » 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. Furthermore, 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.« less
  • 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 » 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. Furthermore, 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.« less
  • 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 » 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.« less
  • Here, the Dark Energy Survey is undertaking an observational programme imaging 1/4 of the southern hemisphere sky with unprecedented photometric accuracy. In the process of observing millions of faint stars and galaxies to constrain the parameters of the dark energy equation of state, the Dark Energy Survey will obtain pre-discovery images of the regions surrounding an estimated 100 gamma-ray bursts over 5 yr. Once gamma-ray bursts are detected by, e.g., the Swift satellite, the DES data will be extremely useful for follow-up observations by the transient astronomy community. We describe a recently-commissioned suite of software that listens continuously for automatedmore » notices of gamma-ray burst activity, collates information from archival DES data, and disseminates relevant data products back to the community in near-real-time. Of particular importance are the opportunities that non-public DES data provide for relative photometry of the optical counterparts of gamma-ray bursts, as well as for identifying key characteristics (e.g., photometric redshifts) of potential gamma-ray burst host galaxies. We provide the functional details of the DESAlert software, and its data products, and we show sample results from the application of DESAlert to numerous previously detected gamma-ray bursts, including the possible identification of several heretofore unknown gamma-ray burst hosts.« less