The Dark Energy Survey 5-year photometrically identified Type Ia Supernovae

A. Möller*, M. Smith, M. Sako, M. Sullivan, M. Vincenzi, P. Wiseman, P. Armstrong, J. Asorey, D. Brout, D. Carollo, T. M. Davis, C. Frohmaier, L. Galbany, K. Glazebrook, L. Kelsey, R. Kessler, G. F. Lewis, C. Lidman, U. Malik, R. C. NicholD. Scolnic, B. E. Tucker, T. M.C. Abbott, M. Aguena, S. Allam, J. Annis, E. Bertin, S. Bocquet, D. Brooks, D. L. Burke, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, F. J. Castander, C. Conselice, M. Costanzi, M. Crocce, L. N. Da Costa, J. De Vicente, S. Desai, H. T. Diehl, P. Doel, S. Everett, I. Ferrero, D. A. Finley, B. Flaugher, D. Friedel, J. Frieman, J. García-Bellido, D. W. Gerdes, D. Gruen, R. A. Gruendl, J. Gschwend, G. Gutierrez, K. Herner, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. J. James, K. Kuehn, N. Kuropatkin, O. Lahav, M. March, J. L. Marshall, F. Menanteau, R. Miquel, R. Morgan, A. Palmese, F. Paz-Chinchón, A. Pieres, A. A. Plazas Malagón, A. K. Romer, A. Roodman, E. Sanchez, V. Scarpine, M. Schubnell, S. Serrano, I. Sevilla-Noarbe, E. Suchyta, G. Tarle, D. Thomas, C. To, T. N. Varga

*Corresponding author for this work

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Abstract

As part of the cosmology analysis using Type Ia Supernovae (SN Ia) in the Dark Energy Survey (DES), we present photometrically identified SN Ia samples using multi-band light-curves and host galaxy redshifts. For this analysis, we use the photometric classification framework SuperNNova (SNN; M\"oller et al. 2019) trained on realistic DES-like simulations. For reliable classification, we process the DES SN programme (DES-SN) data and introduce improvements to the classifier architecture, obtaining classification accuracies of more than 98 per cent on simulations. This is the first SN classification to make use of ensemble methods, resulting in more robust samples. Using photometry, host galaxy redshifts, and a classification probability requirement, we identify 1,863 SNe Ia from which we select 1,484 cosmology-grade SNe Ia spanning the redshift range of 0.07 < z < 1.14. We find good agreement between the light-curve properties of the photometrically-selected sample and simulations. Additionally, we create similar SN Ia samples using two types of Bayesian Neural Network classifiers that provide uncertainties on the classification probabilities. We test the feasibility of using these uncertainties as indicators for out-of-distribution candidates and model confidence. Finally, we discuss the implications of photometric samples and classification methods for future surveys such as Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST).
Original languageEnglish
Pages (from-to)5159–5177
Number of pages19
JournalMonthly Notices of the Royal Astronomical Society
Volume514
Issue number4
Early online date20 Jun 2022
DOIs
Publication statusPublished - 1 Aug 2022

Keywords

  • astro-ph.CO
  • astro-ph.IM
  • methods: data analysis
  • surveys
  • supernovae: general
  • cosmology: observations
  • UKRI
  • STFC
  • MRC
  • MR/T01881X/1

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