| Title: |
DeepZipper. II. Searching for Lensed Supernovae in Dark Energy Survey Data with Deep Learning |
| 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 |
| Source: |
Astrophysical Journal , 943 (1) , Article 19. (2023) |
| Publisher Information: |
American Astronomical Society |
| Publication Year: |
2023 |
| Collection: |
University College London: UCL Discovery |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
https://discovery.ucl.ac.uk/id/eprint/10164895/1/Morgan_2023_ApJ_943_19.pdf; https://discovery.ucl.ac.uk/id/eprint/10164895/ |
| Availability: |
https://discovery.ucl.ac.uk/id/eprint/10164895/1/Morgan_2023_ApJ_943_19.pdf; https://discovery.ucl.ac.uk/id/eprint/10164895/ |
| Rights: |
open |
| Accession Number: |
edsbas.3A181887 |
| Database: |
BASE |