Morgan, R;
Nord, B;
Bechtol, K;
Möller, A;
Hartley, WG;
Birrer, S;
González, SJ;
... Varga, TN; + view all
(2023)
DeepZipper. II. Searching for Lensed Supernovae in Dark Energy Survey Data with Deep Learning.
Astrophysical Journal
, 943
(1)
, Article 19. 10.3847/1538-4357/ac721b.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | DeepZipper. II. Searching for Lensed Supernovae in Dark Energy Survey Data with Deep Learning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3847/1538-4357/ac721b |
| Publisher version: | https://doi.org/10.3847/1538-4357/ac721b |
| Language: | English |
| Additional information: | 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/ |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10164895 |
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