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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:
 [1];  [2];  [3];  [4];  [5];  [6];  [7];  [2];  [7];  [2];  [3];  [8];  [8];  [7];  [9];  [3];  [10];  [11];  [10];  [12] more »;  [10];  [13];  [14];  [15];  [16];  [17];  [18];  [9];  [10];  [19];  [20];  [9];  [9];  [14];  [21];  [22];  [23];  [24];  [25];  [26];  [9];  [10];  [17];  [13];  [27];  [3];  [17];  [28];  [9];  [29];  [9];  [13];  [30];  [31];  [32];  [33];  [9];  [22];  [34];  [35];  [9];  [36];  [28];  [16];  [22];  [4];  [35] « less
+ Show Author Affiliations
  1. Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Univ. of Portsmouth, Portsmouth (United Kingdom)
  3. Univ. of Pennsylvania, Philadelphia, PA (United States)
  4. Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States)
  5. Argonne National Lab. (ANL), Argonne, IL (United States)
  6. Univ. of Chicago,Chicago, IL (United States)
  7. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  8. Univ. of Southampton, Southampton (United Kingdom)
  9. Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
  10. Univ. College London, London (United Kingdom)
  11. Univ. of Cambridge, Cambridge (United Kingdom)
  12. Univ. Pierre et Marie Curie, and CNRS, Paris (France)
  13. Lab. Interinstitucional de e-Astronomia - LIneA, Rio de Janeiro (Brazil); Observatorio Nacional, Rio de Janieiro (Brazil)
  14. Institut de Ciencies de l'Espai, Barcelona (Spain)
  15. Lab. Interinstitucional de e-Astonomia, Rio de Janeiro (Brazil); Observatorio Nacional, Rio de Janeiro (Brazil)
  16. National Center for Supercomputing Applications, Urbana, IL (United States)
  17. Texas A & M Univ., College Station, TX (United States)
  18. Ludwig Maximilian Univ., Muenchen (Germany)
  19. Univ. of Pennsylvania, Philadelphia, PA (United States); California Inst. of Technology (CalTech), Pasadena, CA (United States)
  20. Lab. Interinstitucional de e-Astronomia LIneA, Rio de Janeiro (Brazil)
  21. Univ. of Chicago,Chicago, IL (United States); Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
  22. Univ. of Michigan, Ann Arbor, MI (United States)
  23. Max Planck Institute for Extraterrestrial Physics, Garching (Germany); Univ. Observatory Munich, Munich (Germany)
  24. Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States); National Center for Supercomputing Applications, Urbana, IL (United States)
  25. National Optical Astronomy Observatory, La Serena (Chile)
  26. Austrilian Astronomical Observatory, North Ryde (Australia)
  27. Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro (Brazil)
  28. The Ohio State Univ., Columbus, OH (United States)
  29. Univ. Autonoma de Barcelona, Barcelona (Spain); Institucio Catalana de Recerca i Estudis, Barcelona (Spain)
  30. California Inst. of Technology (CalTech), Pasadena, CA (United States); Univ. of Michigan, Ann Arbor, MI (United States)
  31. Univ. of Sussex, Brighton (United Kingdom)
  32. Stanford Univ., Stanford, CA (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States)
  33. Centro de Investigaciones Energeticas, Madrid (Spain)
  34. Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States); Centro de Investigaciones Energeticas, Madrid (Spain)
  35. National Optical Astronomy Observatory, North Ryde (Australia)
  36. Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); Lab. Interinstitucional de e-Astronomia LIneA, Rio de Janeiro (Brazil)
Publication Date:
Research Org.:
Brookhaven National Laboratory (BNL), Upton, NY (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP)
OSTI Identifier:
1234374
Alternate Identifier(s):
OSTI ID: 1311819; OSTI ID: 1812341
Report Number(s):
BNL-111654-2015-JA; SLAC-PUB-16709
Journal ID: ISSN 1538-3881; KA2301020
Grant/Contract Number:  
SC00112704; AC02-76SF00515; AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
The Astronomical Journal (Online)
Additional Journal Information:
Journal Name: The Astronomical Journal (Online); Journal Volume: 150; Journal Issue: 3; 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; 97 MATHEMATICS AND COMPUTING; discovery; algorithms; statistical; random forest

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., Nungent, P., Papadopoulos, A., Sako, M., Smith, M., Sullivan, M., Thomas, R. C., Wester, W., Wolf, R. C., Abdalla, F. B., Banjeri, M., Benoit-Levy, A., Bertin, E., Brooks, D., Rosell, A. Carnero, Castander, F. J., da Costa, L. N., 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.
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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., Nungent, P., Papadopoulos, A., Sako, M., Smith, M., Sullivan, M., Thomas, R. C., Wester, W., Wolf, R. C., Abdalla, F. B., Banjeri, M., Benoit-Levy, A., Bertin, E., Brooks, D., Rosell, A. Carnero, Castander, F. J., da Costa, L. N., 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. https://doi.org/10.1088/0004-6256/150/3/82
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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., Nungent, P., Papadopoulos, A., Sako, M., Smith, M., Sullivan, M., Thomas, R. C., Wester, W., Wolf, R. C., Abdalla, F. B., Banjeri, M., Benoit-Levy, A., Bertin, E., Brooks, D., Rosell, A. Carnero, Castander, F. J., da Costa, L. N., 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. Tue . "Automated transient identification in the Dark Energy Survey". United States. https://doi.org/10.1088/0004-6256/150/3/82. https://www.osti.gov/servlets/purl/1234374.
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@article{osti_1234374,
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 Nungent, P. 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 Banjeri, M. and Benoit-Levy, A. and Bertin, E. and Brooks, D. and Rosell, A. Carnero and Castander, F. J. and da Costa, L. N. 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 = {The Astronomical Journal (Online)},
number = 3,
volume = 150,
place = {United States},
year = {Tue Sep 01 00:00:00 EDT 2015},
month = {Tue Sep 01 00:00:00 EDT 2015}
}
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    Studying the Ultraviolet Spectrum of the First Spectroscopically Confirmed Supernova at Redshift Two
    journal, February 2018

    An analysis of feature relevance in the classification of astronomical transients with machine learning methods
    journal, February 2016

    • D'Isanto, A.; Cavuoti, S.; Brescia, M.
    • Monthly Notices of the Royal Astronomical Society, Vol. 457, Issue 3
    • DOI: 10.1093/mnras/stw157

    First Cosmology Results Using Type Ia Supernovae from the Dark Energy Survey: Photometric Pipeline and Light-curve Data Release
    journal, March 2019

    First cosmology results using Type Ia supernova from the Dark Energy Survey: simulations to correct supernova distance biases
    journal, February 2019

    • Kessler, R.; Brout, D.; D’Andrea, C. B.
    • Monthly Notices of the Royal Astronomical Society, Vol. 485, Issue 1
    • DOI: 10.1093/mnras/stz463

    Rapidly evolving transients in the Dark Energy Survey
    text, January 2018

    Quasar Accretion Disk Sizes from Continuum Reverberation Mapping from the Dark Energy Survey
    text, January 2018

    • Mudd, D.; Martini, P.; Zu, Y.
    • Apollo - University of Cambridge Repository
    • DOI: 10.17863/cam.20889

    Superluminous supernovae from the Dark Energy Survey
    journal, May 2019

    • Angus, C. R.; Smith, M.; Sullivan, M.
    • Monthly Notices of the Royal Astronomical Society, Vol. 487, Issue 2
    • DOI: 10.1093/mnras/stz1321
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