Details

Autor(en) / Beteiligte

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

Titel

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

Ist Teil von

• The Astrophysical journal, 2023-01, Vol.943 (1), p.19

Ort / Verlag

Philadelphia: The American Astronomical Society

Erscheinungsjahr

2023

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• 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.