In:
The Astrophysical Journal, American Astronomical Society, Vol. 943, No. 1 ( 2023-01-01), p. 19-
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 of Medium:
Online Resource
ISSN:
0004-637X
,
1538-4357
DOI:
10.3847/1538-4357/ac721b
Language:
Unknown
Publisher:
American Astronomical Society
Publication Date:
2023
detail.hit.zdb_id:
2207648-7
detail.hit.zdb_id:
1473835-1
SSG:
16,12
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