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

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

doi:10.3847/1538-4357/ac721b

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

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

Research output: Contribution to journal › Article › peer-review

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.

Original language	English (US)
Article number	19
Journal	Astrophysical Journal
Volume	943
Issue number	1
DOIs	https://doi.org/10.3847/1538-4357/ac721b
State	Published - Jan 1 2023

ASJC Scopus subject areas

Astronomy and Astrophysics
Space and Planetary Science

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10.3847/1538-4357/ac721b

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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. C., Lidman, C., Collett, T., Abbott, T. M. C., Aguena, M., Andrade-Oliveira, F., Annis, J., Bacon, D., ... Varga, T. N. (2023). DeepZipper. II. Searching for Lensed Supernovae in Dark Energy Survey Data with Deep Learning. Astrophysical Journal, 943(1), Article 19. https://doi.org/10.3847/1538-4357/ac721b

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 2023, 'DeepZipper. II. Searching for Lensed Supernovae in Dark Energy Survey Data with Deep Learning', Astrophysical Journal, vol. 943, no. 1, 19. https://doi.org/10.3847/1538-4357/ac721b

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title = "DeepZipper. II. Searching for Lensed Supernovae in Dark Energy Survey Data with Deep Learning",

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.",

author = "R. Morgan and B. Nord and K. Bechtol and A. M{\"o}ller and Hartley, {W. G.} and S. Birrer and Gonz{\'a}lez, {S. J.} and M. Martinez and Gruendl, {R. A.} and Buckley-Geer, {E. J.} and Shajib, {A. J.} and Rosell, {A. Carnero} and C. Lidman and T. Collett and Abbott, {T. M.C.} and M. Aguena and F. Andrade-Oliveira and J. Annis and D. Bacon and S. Bocquet and D. Brooks and Burke, {D. L.} and Kind, {M. Carrasco} and J. Carretero and Castander, {F. J.} and C. Conselice and Costa, {L. N.da} and M. Costanzi and {De Vicente}, J. and S. Desai and P. Doel and S. Everett and I. Ferrero and B. Flaugher and D. Friedel and J. Frieman and J. Garc{\'i}a-Bellido and E. Gaztanaga and D. Gruen and G. Gutierrez and Hinton, {S. R.} and Hollowood, {D. L.} and K. Honscheid and K. Kuehn and N. Kuropatkin and O. Lahav and M. Lima and F. Menanteau and R. Miquel and A. Palmese and F. Paz-Chinch{\'o}n and Pereira, {M. E.S.} and A. Pieres and Malag{\'o}n, {A. A.Plazas} and J. Prat and M. Rodriguez-Monroy and Romer, {A. K.} and A. Roodman and E. Sanchez and V. Scarpine and I. Sevilla-Noarbe and M. Smith and E. Suchyta and Swanson, {M. E.C.} and G. Tarle and D. Thomas and Varga, {T. N.}",

note = "Publisher Copyright: {\textcopyright} 2023. The Author(s). Published by the American Astronomical Society.",

year = "2023",

month = jan,

day = "1",

doi = "10.3847/1538-4357/ac721b",

language = "English (US)",

volume = "943",

journal = "Astrophysical Journal",

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publisher = "American Astronomical Society",

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TY - JOUR

T1 - DeepZipper. II. Searching for Lensed Supernovae in Dark Energy Survey Data with Deep Learning

AU - Morgan, R.

AU - Nord, B.

AU - Bechtol, K.

AU - Möller, A.

AU - Hartley, W. G.

AU - Birrer, S.

AU - González, S. J.

AU - Martinez, M.

AU - Gruendl, R. A.

AU - Buckley-Geer, E. J.

AU - Shajib, A. J.

AU - Rosell, A. Carnero

AU - Lidman, C.

AU - Collett, T.

AU - Abbott, T. M.C.

AU - Aguena, M.

AU - Andrade-Oliveira, F.

AU - Annis, J.

AU - Bacon, D.

AU - Bocquet, S.

AU - Brooks, D.

AU - Burke, D. L.

AU - Kind, M. Carrasco

AU - Carretero, J.

AU - Castander, F. J.

AU - Conselice, C.

AU - Costa, L. N.da

AU - Costanzi, M.

AU - De Vicente, J.

AU - Desai, S.

AU - Doel, P.

AU - Everett, S.

AU - Ferrero, I.

AU - Flaugher, B.

AU - Friedel, D.

AU - Frieman, J.

AU - García-Bellido, J.

AU - Gaztanaga, E.

AU - Gruen, D.

AU - Gutierrez, G.

AU - Hinton, S. R.

AU - Hollowood, D. L.

AU - Honscheid, K.

AU - Kuehn, K.

AU - Kuropatkin, N.

AU - Lahav, O.

AU - Lima, M.

AU - Menanteau, F.

AU - Miquel, R.

AU - Palmese, A.

AU - Paz-Chinchón, F.

AU - Pereira, M. E.S.

AU - Pieres, A.

AU - Malagón, A. A.Plazas

AU - Prat, J.

AU - Rodriguez-Monroy, M.

AU - Romer, A. K.

AU - Roodman, A.

AU - Sanchez, E.

AU - Scarpine, V.

AU - Sevilla-Noarbe, I.

AU - Smith, M.

AU - Suchyta, E.

AU - Swanson, M. E.C.

AU - Tarle, G.

AU - Thomas, D.

AU - Varga, T. N.

PY - 2023/1/1

Y1 - 2023/1/1

N2 - 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.

AB - 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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UR - http://www.scopus.com/inward/citedby.url?scp=85147151285&partnerID=8YFLogxK

U2 - 10.3847/1538-4357/ac721b

DO - 10.3847/1538-4357/ac721b

M3 - Article

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SN - 0004-637X

VL - 943

JO - Astrophysical Journal

JF - Astrophysical Journal

IS - 1

M1 - 19

ER -

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

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