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Publication Detail
DeepZipper. II. Searching for Lensed Supernovae in Dark Energy Survey Data with Deep Learning
  • Publication Type:
    Journal article
  • Authors:
    Morgan R, Nord B, Bechtol K, Möller A, Hartley WG, Birrer S, González SJ, Martinez M, Gruendl RA, Buckley-Geer EJ, Shajib AJ, Rosell AC, Lidman C, Collett T, Abbott TMC, Aguena M, Andrade-Oliveira F, Annis J, Bacon D, Bocquet S, Brooks D, Burke DL, Kind MC, Carretero J, Castander FJ, Conselice C, Costa LND, Costanzi M, De Vicente J, Desai S, Doel P, Everett S, Ferrero I, Flaugher B, Friedel D, Frieman J, García-Bellido J, Gaztanaga E, Gruen D, Gutierrez G, Hinton SR, Hollowood DL, Honscheid K, Kuehn K, Kuropatkin N, Lahav O, Lima M, Menanteau F, Miquel R, Palmese A, Paz-Chinchón F, Pereira MES, Pieres A, Malagón AAP, Prat J, Rodriguez-Monroy M, Romer AK, Roodman A, Sanchez E, Scarpine V, Sevilla-Noarbe I, Smith M, Suchyta E, Swanson MEC, Tarle G, Thomas D, Varga TN
  • Publisher:
    American Astronomical Society
  • Publication date:
  • Journal:
    Astrophysical Journal
  • Volume:
  • Issue:
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  • Notes:
    This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
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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