Finding quadruply imaged quasars with machine learning-I. Methods
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1093/mnras/stac925 http://hdl.handle.net/11449/241025 |
Resumo: | Strongly lensed quadruply imaged quasars (quads) are extraordinary objects. They are very rare in the sky and yet they provide unique information about a wide range of topics, including the expansion history and the composition of the Universe, the distribution of stars and dark matter in galaxies, the host galaxies of quasars, and the stellar initial mass function. Finding them in astronomical images is a classic 'needle in a haystack' problem, as they are outnumbered by other (contaminant) sources by many orders of magnitude. To solve this problem, we develop state-of-the-art deep learning methods and train them on realistic simulated quads based on real images of galaxies taken from the Dark Energy Survey, with realistic source and deflector models, including the chromatic effects of microlensing. The performance of the best methods on a mixture of simulated and real objects is excellent, yielding area under the receiver operating curve in the range of 0.86-0.89. Recall is close to 100 per cent down to total magnitude i ∼21 indicating high completeness, while precision declines from 85 per cent to 70 per cent in the range i ∼17-21. The methods are extremely fast: Training on 2 million samples takes 20 h on a GPU machine, and 108 multiband cut-outs can be evaluated per GPU-hour. The speed and performance of the method pave the way to apply it to large samples of astronomical sources, bypassing the need for photometric pre-selection that is likely to be a major cause of incompleteness in current samples of known quads. |
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Finding quadruply imaged quasars with machine learning-I. Methodsastronomical data bases: Surveysgravitational lensing: Strongmethods: StatisticalStrongly lensed quadruply imaged quasars (quads) are extraordinary objects. They are very rare in the sky and yet they provide unique information about a wide range of topics, including the expansion history and the composition of the Universe, the distribution of stars and dark matter in galaxies, the host galaxies of quasars, and the stellar initial mass function. Finding them in astronomical images is a classic 'needle in a haystack' problem, as they are outnumbered by other (contaminant) sources by many orders of magnitude. To solve this problem, we develop state-of-the-art deep learning methods and train them on realistic simulated quads based on real images of galaxies taken from the Dark Energy Survey, with realistic source and deflector models, including the chromatic effects of microlensing. The performance of the best methods on a mixture of simulated and real objects is excellent, yielding area under the receiver operating curve in the range of 0.86-0.89. Recall is close to 100 per cent down to total magnitude i ∼21 indicating high completeness, while precision declines from 85 per cent to 70 per cent in the range i ∼17-21. The methods are extremely fast: Training on 2 million samples takes 20 h on a GPU machine, and 108 multiband cut-outs can be evaluated per GPU-hour. The speed and performance of the method pave the way to apply it to large samples of astronomical sources, bypassing the need for photometric pre-selection that is likely to be a major cause of incompleteness in current samples of known quads.Department of Electrical and Computer Engineering University of CaliforniaDepartment of Electrical and Computer Engineering Nazarbayev UniversityInter-University Centre for Astronomy and Astrophysics (IUCAA), Post Bag 4Kavli IPMU (WPI) UTIAS University of TokyoDepartment of Statistics University of CaliforniaDepartment of Political Science University of CaliforniaDepartment of Physics and Astronomy, PAB, 430 Portola Plaza, Box 951547Kavli Institute for Particle Astrophysics and Cosmology Department of Physics Stanford UniversityDepartment of Astronomy & Astrophysics University of ChicagoSchool of Physics and Technology Wuhan UniversityLaboratoire d'Astrophysique Ecole Polytechnique Federale de Lausanne (EPFL) Observatoire de SauvernyDARK Niels Bohr Institute, Jagtvej 128Fermi National Accelerator Laboratory, PO Box 500Kavli Institute for Cosmolo gical Physics Univer sity of Chica goLaboratorio Interinstitucional de e-Astronomia-LIneA, Rua Gal. Jose Cristino 77Instituto de Fisica Teorica Universidade Estadual PaulistaDepartment of Physics & Astronomy University College London, Gower StreetKavli Institute for Particle Astrophysics & Cosmology Stanford University, PO Box 2450SLAC National Accelerator LaboratoryCenter for Astrophysical Surveys National Center for Supercomputing Applications, 1205 West Clark St.Department of Astronomy University of Illinois at Urbana-Champaign, 1002 W. Green StreetInstitut de Fisica d'Altes Energies (IFAE) Barcelona Institute of Science and Technology Campus UABCenter for Cosmology and Astro-Particle Physics Ohio State UniversityJodrell Bank Center for Astrophysics School of Physics and Astronomy University of Manchester, Oxford RoadUniversity of Nottingham School of Physics and AstronomyAstronomy Unit Department of Physics University of Trieste, Via Tiepolo 11INAF-Osservatorio Astronomico di Trieste, Via G. B. Tiepolo 11Institute for Fundamental Physics of the Universe, Via Beirut 2Observatorio Nacional, Rua Gal. Jos é Cristino 77Department of Physics University of MichiganHamburg er Sternwarte Universitat Hamburg, Gojenbergsweg 112Centro de Investigaciones Energeticas Medioambientales y Tecnologicas (CIEMAT)Department of Physics IIT HyderabadFaculty of Physics Ludwig-Maximilians-Universitat, Scheinerstr. 1Santa Cruz Institute for Particle PhysicsInstituto de Fisica Teorica UAM CSIC Universidad Autonoma de MadridDepartment of Astronomy University of MichiganUniver sit äts-Sternwarte Fakult ät f ür Physik Ludwig-Maximilians Univer-sit ät M ünchen, Scheinerstr. 1School of Mathematics and Physics University of QueenslandDepartment of Physics Ohio State UniversityCenter for Astrophysics | Harvard & Smithsonian, 60 Garden StreetLawrence Berkeley National Laboratory, 1 Cyclotron RoadAustralian Astronomical Optics Macquarie UniversityLowell Observatory, 1400 Mars Hill RdDepartamento de Fisica Matem ática Instituto de Fisica Universidade de S ao Paulo, CP 66318Department of Physics and Astronomy University of PennsylvaniaInstitucio Catalana de Recerca i Estudis Avan catsPhysics Department University of Wisconsin-Madison, 2320 Chamberlin Hall, 1150 University AvenueInstitute of Astronomy University of Cambridge, Madingley RoadDepartment of Astrophysical Sciences Princeton University, Peyton HallInstitut d'Estudis Espacials de Catalunya (IEEC)Institute of Space Sciences (ICE CSIC) Campus UAB, Carrer de Can Magrans, s/nSchool of Physics and Astronomy University of SouthamptonComputer Science and Mathematics Division Oak Ridge National Labo-ratoryDepartment of Physics Stanford University, 382 Via Pueblo MallMax Planck Institute for Extraterrestrial Physics, GiessenbachstrasseInstituto de Fisica Teorica Universidade Estadual PaulistaUniversity of CaliforniaNazarbayev UniversityInter-University Centre for Astronomy and Astrophysics (IUCAA)University of TokyoStanford UniversityUniversity of ChicagoWuhan UniversityObservatoire de SauvernyNiels Bohr InstituteFermi National Accelerator LaboratoryUniver sity of Chica goLaboratorio Interinstitucional de e-Astronomia-LIneAUniversidade Estadual Paulista (UNESP)University College LondonSLAC National Accelerator LaboratoryNational Center for Supercomputing ApplicationsUniversity of Illinois at Urbana-ChampaignBarcelona Institute of Science and TechnologyOhio State UniversityUniversity of ManchesterSchool of Physics and AstronomyUniversity of TriesteINAF-Osservatorio Astronomico di TriesteInstitute for Fundamental Physics of the UniverseObservatorio NacionalUniversity of MichiganUniversitat HamburgMedioambientales y Tecnologicas (CIEMAT)IIT HyderabadLudwig-Maximilians-UniversitatSanta Cruz Institute for Particle PhysicsUniversidad Autonoma de MadridLudwig-Maximilians Univer-sit ät M ünchenUniversity of QueenslandCenter for Astrophysics | Harvard & SmithsonianLawrence Berkeley National LaboratoryMacquarie UniversityLowell ObservatoryUniversidade de S ao PauloUniversity of PennsylvaniaInstitucio Catalana de Recerca i Estudis Avan catsUniversity of Wisconsin-MadisonUniversity of CambridgePrinceton UniversityInstitut d'Estudis Espacials de Catalunya (IEEC)CSIC)University of SouthamptonOak Ridge National Labo-ratoryMax Planck Institute for Extraterrestrial PhysicsAkhazhanov, A.More, A.Amini, A.Hazlett, C.Treu, T.Birrer, S.Shajib, A.Liao, K.Lemon, C.Agnello, A.Nord, B.Aguena, M.Allam, S.Andrade-Oliveira, F. [UNESP]Annis, J.Brooks, D.Buckley-Geer, E.Burke, D. L.Carnero Rosell, A.Carrasco Kind, M.Carretero, J.Choi, A.Conselice, C.Costanzi, M.Costa, L. N. daPereira, M. E. S.Vicente, J. deDesai, S.Dietrich, J. P.Doel, P.Everett, S.Ferrero, I.Finley, D. A.Flaugher, B.Frieman, J.Garciá-Bellido, J.Gerdes, D. W.Gruen, D.Gruendl, R. A.Gschwend, J.Gutierrez, G.Hinton, S. R.Hollowood, D. L.Honscheid, K.James, D. J.Kim, A. G.Kuehn, K.Kuropatkin, N.Lahav, O.Lima, M.Lin, H.Maia, M. A.G.March, M.Menanteau, F.Miquel, R.Morgan, R.Palmese, A.Paz-Chinchón, F.Pieres, A.Plazas Malagón, A. A.Sanchez, E.Scarpine, V.Serrano, S.Sevilla-Noarbe, I.Smith, M.Soares-Santos, M.Suchyta, E.Swanson, M. E. C.Tarle, G.To, C.Varga, T. N.Weller, J.2023-03-01T20:43:42Z2023-03-01T20:43:42Z2022-06-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article2407-2421http://dx.doi.org/10.1093/mnras/stac925Monthly Notices of the Royal Astronomical Society, v. 513, n. 2, p. 2407-2421, 2022.1365-29660035-8711http://hdl.handle.net/11449/24102510.1093/mnras/stac9252-s2.0-85130484125Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengMonthly Notices of the Royal Astronomical Societyinfo:eu-repo/semantics/openAccess2023-03-01T20:43:43Zoai:repositorio.unesp.br:11449/241025Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-03-01T20:43:43Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Finding quadruply imaged quasars with machine learning-I. Methods |
title |
Finding quadruply imaged quasars with machine learning-I. Methods |
spellingShingle |
Finding quadruply imaged quasars with machine learning-I. Methods Akhazhanov, A. astronomical data bases: Surveys gravitational lensing: Strong methods: Statistical |
title_short |
Finding quadruply imaged quasars with machine learning-I. Methods |
title_full |
Finding quadruply imaged quasars with machine learning-I. Methods |
title_fullStr |
Finding quadruply imaged quasars with machine learning-I. Methods |
title_full_unstemmed |
Finding quadruply imaged quasars with machine learning-I. Methods |
title_sort |
Finding quadruply imaged quasars with machine learning-I. Methods |
author |
Akhazhanov, A. |
author_facet |
Akhazhanov, A. More, A. Amini, A. Hazlett, C. Treu, T. Birrer, S. Shajib, A. Liao, K. Lemon, C. Agnello, A. Nord, B. Aguena, M. Allam, S. Andrade-Oliveira, F. [UNESP] Annis, J. Brooks, D. Buckley-Geer, E. Burke, D. L. Carnero Rosell, A. Carrasco Kind, M. Carretero, J. Choi, A. Conselice, C. Costanzi, M. Costa, L. N. da Pereira, M. E. S. Vicente, J. de Desai, S. Dietrich, J. P. Doel, P. Everett, S. Ferrero, I. Finley, D. A. Flaugher, B. Frieman, J. Garciá-Bellido, J. Gerdes, D. W. Gruen, D. Gruendl, R. A. Gschwend, J. Gutierrez, G. Hinton, S. R. Hollowood, D. L. Honscheid, K. James, D. J. Kim, A. G. Kuehn, K. Kuropatkin, N. Lahav, O. Lima, M. Lin, H. Maia, M. A.G. March, M. Menanteau, F. Miquel, R. Morgan, R. Palmese, A. Paz-Chinchón, F. Pieres, A. Plazas Malagón, A. A. Sanchez, E. Scarpine, V. Serrano, S. Sevilla-Noarbe, I. Smith, M. Soares-Santos, M. Suchyta, E. Swanson, M. E. C. Tarle, G. To, C. Varga, T. N. Weller, J. |
author_role |
author |
author2 |
More, A. Amini, A. Hazlett, C. Treu, T. Birrer, S. Shajib, A. Liao, K. Lemon, C. Agnello, A. Nord, B. Aguena, M. Allam, S. Andrade-Oliveira, F. [UNESP] Annis, J. Brooks, D. Buckley-Geer, E. Burke, D. L. Carnero Rosell, A. Carrasco Kind, M. Carretero, J. Choi, A. Conselice, C. Costanzi, M. Costa, L. N. da Pereira, M. E. S. Vicente, J. de Desai, S. Dietrich, J. P. Doel, P. Everett, S. Ferrero, I. Finley, D. A. Flaugher, B. Frieman, J. Garciá-Bellido, J. Gerdes, D. W. Gruen, D. Gruendl, R. A. Gschwend, J. Gutierrez, G. Hinton, S. R. Hollowood, D. L. Honscheid, K. James, D. J. Kim, A. G. Kuehn, K. Kuropatkin, N. Lahav, O. Lima, M. Lin, H. Maia, M. A.G. March, M. Menanteau, F. Miquel, R. Morgan, R. Palmese, A. Paz-Chinchón, F. Pieres, A. Plazas Malagón, A. A. Sanchez, E. Scarpine, V. Serrano, S. Sevilla-Noarbe, I. Smith, M. Soares-Santos, M. Suchyta, E. Swanson, M. E. C. Tarle, G. To, C. Varga, T. N. Weller, J. |
author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
University of California Nazarbayev University Inter-University Centre for Astronomy and Astrophysics (IUCAA) University of Tokyo Stanford University University of Chicago Wuhan University Observatoire de Sauverny Niels Bohr Institute Fermi National Accelerator Laboratory Univer sity of Chica go Laboratorio Interinstitucional de e-Astronomia-LIneA Universidade Estadual Paulista (UNESP) University College London SLAC National Accelerator Laboratory National Center for Supercomputing Applications University of Illinois at Urbana-Champaign Barcelona Institute of Science and Technology Ohio State University University of Manchester School of Physics and Astronomy University of Trieste INAF-Osservatorio Astronomico di Trieste Institute for Fundamental Physics of the Universe Observatorio Nacional University of Michigan Universitat Hamburg Medioambientales y Tecnologicas (CIEMAT) IIT Hyderabad Ludwig-Maximilians-Universitat Santa Cruz Institute for Particle Physics Universidad Autonoma de Madrid Ludwig-Maximilians Univer-sit ät M ünchen University of Queensland Center for Astrophysics | Harvard & Smithsonian Lawrence Berkeley National Laboratory Macquarie University Lowell Observatory Universidade de S ao Paulo University of Pennsylvania Institucio Catalana de Recerca i Estudis Avan cats University of Wisconsin-Madison University of Cambridge Princeton University Institut d'Estudis Espacials de Catalunya (IEEC) CSIC) University of Southampton Oak Ridge National Labo-ratory Max Planck Institute for Extraterrestrial Physics |
dc.contributor.author.fl_str_mv |
Akhazhanov, A. More, A. Amini, A. Hazlett, C. Treu, T. Birrer, S. Shajib, A. Liao, K. Lemon, C. Agnello, A. Nord, B. Aguena, M. Allam, S. Andrade-Oliveira, F. [UNESP] Annis, J. Brooks, D. Buckley-Geer, E. Burke, D. L. Carnero Rosell, A. Carrasco Kind, M. Carretero, J. Choi, A. Conselice, C. Costanzi, M. Costa, L. N. da Pereira, M. E. S. Vicente, J. de Desai, S. Dietrich, J. P. Doel, P. Everett, S. Ferrero, I. Finley, D. A. Flaugher, B. Frieman, J. Garciá-Bellido, J. Gerdes, D. W. Gruen, D. Gruendl, R. A. Gschwend, J. Gutierrez, G. Hinton, S. R. Hollowood, D. L. Honscheid, K. James, D. J. Kim, A. G. Kuehn, K. Kuropatkin, N. Lahav, O. Lima, M. Lin, H. Maia, M. A.G. March, M. Menanteau, F. Miquel, R. Morgan, R. Palmese, A. Paz-Chinchón, F. Pieres, A. Plazas Malagón, A. A. Sanchez, E. Scarpine, V. Serrano, S. Sevilla-Noarbe, I. Smith, M. Soares-Santos, M. Suchyta, E. Swanson, M. E. C. Tarle, G. To, C. Varga, T. N. Weller, J. |
dc.subject.por.fl_str_mv |
astronomical data bases: Surveys gravitational lensing: Strong methods: Statistical |
topic |
astronomical data bases: Surveys gravitational lensing: Strong methods: Statistical |
description |
Strongly lensed quadruply imaged quasars (quads) are extraordinary objects. They are very rare in the sky and yet they provide unique information about a wide range of topics, including the expansion history and the composition of the Universe, the distribution of stars and dark matter in galaxies, the host galaxies of quasars, and the stellar initial mass function. Finding them in astronomical images is a classic 'needle in a haystack' problem, as they are outnumbered by other (contaminant) sources by many orders of magnitude. To solve this problem, we develop state-of-the-art deep learning methods and train them on realistic simulated quads based on real images of galaxies taken from the Dark Energy Survey, with realistic source and deflector models, including the chromatic effects of microlensing. The performance of the best methods on a mixture of simulated and real objects is excellent, yielding area under the receiver operating curve in the range of 0.86-0.89. Recall is close to 100 per cent down to total magnitude i ∼21 indicating high completeness, while precision declines from 85 per cent to 70 per cent in the range i ∼17-21. The methods are extremely fast: Training on 2 million samples takes 20 h on a GPU machine, and 108 multiband cut-outs can be evaluated per GPU-hour. The speed and performance of the method pave the way to apply it to large samples of astronomical sources, bypassing the need for photometric pre-selection that is likely to be a major cause of incompleteness in current samples of known quads. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-06-01 2023-03-01T20:43:42Z 2023-03-01T20:43:42Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1093/mnras/stac925 Monthly Notices of the Royal Astronomical Society, v. 513, n. 2, p. 2407-2421, 2022. 1365-2966 0035-8711 http://hdl.handle.net/11449/241025 10.1093/mnras/stac925 2-s2.0-85130484125 |
url |
http://dx.doi.org/10.1093/mnras/stac925 http://hdl.handle.net/11449/241025 |
identifier_str_mv |
Monthly Notices of the Royal Astronomical Society, v. 513, n. 2, p. 2407-2421, 2022. 1365-2966 0035-8711 10.1093/mnras/stac925 2-s2.0-85130484125 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Monthly Notices of the Royal Astronomical Society |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
2407-2421 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
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1799964972503007232 |