The Astrophysical Journal (Jan 2023)

DeepZipper. II. Searching for Lensed Supernovae in Dark Energy Survey Data with Deep Learning

  • R. Morgan,
  • B. Nord,
  • K. Bechtol,
  • A. Möller,
  • W. G. Hartley,
  • S. Birrer,
  • S. J. González,
  • M. Martinez,
  • R. A. Gruendl,
  • E. J. Buckley-Geer,
  • A. J. Shajib,
  • A. Carnero Rosell,
  • C. Lidman,
  • T. Collett,
  • T. M. C. Abbott,
  • M. Aguena,
  • F. Andrade-Oliveira,
  • J. Annis,
  • D. Bacon,
  • S. Bocquet,
  • D. Brooks,
  • D. L. Burke,
  • M. Carrasco Kind,
  • J. Carretero,
  • F. J. Castander,
  • C. Conselice,
  • L. N. da Costa,
  • M. Costanzi,
  • J. De Vicente,
  • S. Desai,
  • P. Doel,
  • S. Everett,
  • I. Ferrero,
  • B. Flaugher,
  • D. Friedel,
  • J. Frieman,
  • J. García-Bellido,
  • E. Gaztanaga,
  • D. Gruen,
  • G. Gutierrez,
  • S. R. Hinton,
  • D. L. Hollowood,
  • K. Honscheid,
  • K. Kuehn,
  • N. Kuropatkin,
  • O. Lahav,
  • M. Lima,
  • F. Menanteau,
  • R. Miquel,
  • A. Palmese,
  • F. Paz-Chinchón,
  • M. E. S. Pereira,
  • A. Pieres,
  • A. A. Plazas Malagón,
  • J. Prat,
  • M. Rodriguez-Monroy,
  • A. K. Romer,
  • A. Roodman,
  • E. Sanchez,
  • V. Scarpine,
  • I. Sevilla-Noarbe,
  • M. Smith,
  • E. Suchyta,
  • M. E. C. Swanson,
  • G. Tarle,
  • D. Thomas,
  • T. N. Varga

DOI
https://doi.org/10.3847/1538-4357/ac721b
Journal volume & issue
Vol. 943, no. 1
p. 19

Abstract

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Gravitationally lensed supernovae (LSNe) are important probes of cosmic expansion, but they remain rare and difficult to find. Current cosmic surveys likely contain 5–10 LSNe in total while next-generation experiments are expected to contain several hundred to a few thousand of these systems. We search for these systems in observed Dark Energy Survey (DES) five year SN fields—10 3 sq. deg. regions of sky imaged in the griz bands approximately every six nights over five years. To perform the search, we utilize the DeepZipper approach: a multi-branch deep learning architecture trained on image-level simulations of LSNe that simultaneously learns spatial and temporal relationships from time series of images. We find that our method obtains an LSN recall of 61.13% and a false-positive rate of 0.02% on the DES SN field data. DeepZipper selected 2245 candidates from a magnitude-limited ( m _i < 22.5) catalog of 3,459,186 systems. We employ human visual inspection to review systems selected by the network and find three candidate LSNe in the DES SN fields.

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