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Expediting DECam Multimessenger Counterpart Searches with Convolutional Neural Networks

ASTROPHYSICAL JOURNAL 2022年第1期925卷 44页

作者： Shandonay, A. Morgan, R. Bechtol, K. Bom, C. R. Nord, B. Garcia, A. Henghes, B. Herner, K. Tabbutt, M. Palmese, A. Santana-Silva, L. Soares-Santos, M. Gill, M. S. S. Garcia-Bellido, J. Univ Wisconsin Dept Phys 1150 Univ Ave Madison WI 53706 USA Legacy Survey Space & Time Corp Data Sci Fellowship Program Tucson AZ USA Legacy Survey Space & Time 933 North Cherry Ave Tucson AZ 85721 USA Ctr Brasileiro Pesquisas Fis Rua Dr Xavier Sigaud 150 BR-22290180 Rio De Janeiro RJ Brazil Ctr Fed Educ Tecnol Celso Suckow da Fonseca Rodovia Mario Covas BR-23810000 Itaguai RJ Brazil Fermilab Natl Accelerator Lab POB 500 Batavia IL 60510 USA Univ Chicago Kavli Inst Cosmol Phys Chicago IL 60637 USA Univ Michigan Dept Astron Ann Arbor MI 48109 USA UCL Dept Phys & Astron Gower St London WC1E 6BT England Univ Cidade Sao Paulo NAT Univ Cruzeiro Sul Rua Galvao Bueno 868 BR-01506000 Sao Paulo SP Brazil Univ Michigan Dept Phys Ann Arbor MI 48109 USA SLAC Natl Accelerator Lab Menlo Pk CA 94025 USA Univ Autonoma Madrid Inst Fis Teor UAM CSIC E-28049 Madrid Spain

Searches for counterparts to multimessenger events with optical imagers use difference imaging to detect new transient sources. However, even with existing artifact-detection algorithms, this process simultaneously returns several classes of false positives: false detections from poor-quality image subtractions, false detections from low signal-to-noise images, and detections of preexisting variable sources. Currently, human visual inspection to remove the false positives is a central part of multimessenger follow-up observations, but when next generation gravitational wave and neutrino detectors come online and increase the rate of multimessenger events, the visual inspection process will be prohibitively expensive. We approach this problem with two convolutional neural networks operating on the difference imaging outputs. The first network focuses on removing false detections and demonstrates an accuracy of 92% on our data set. The second network focuses on sorting all real detections by the probability of being a transient source within a host galaxy and distinguishes between various classes of images that previously required additional human inspection. We find the number of images requiring human inspection will decrease by a factor of 1.5 using our approach alone and a factor of 3.6 using our approach in combination with existing algorithms, facilitating rapid multimessenger counterpart identification by the astronomical community.

关键词： Convolutional neural networks Transient detection

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Deeplenstronomy: A dataset simulation package for strong gravitational lensing

arXiv

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arXiv 2021年

作者： Morgan, Robert Nord, Brian Birrer, Simon Lin, Joshua Yao-Yu Poh, Jason University of Wisconsin-Madison United States Legacy Survey of Space and Time Data Science Fellowship Program United States Fermi National Accelerator Laboratory United States University of Chicago United States Stanford University United States University of Illinois Urbana-Champaign United States

deeplenstronomy generates SL datasets by organizing and expediting user interaction with lenstronomy. The user creates a single yaml-style configuration file that describes the aspects of the dataset: number of images, properties of the telescope and camera, cosmological parameters, observing conditions, properties of the physical objects, and geometry of the SL systems. deeplenstronomy parses the configuration file and generates the dataset, producing both the images and the parameters that led to the production of each image as outputs. The configuration files can easily be shared, enabling users to easily reproduce each other's training datasets. The premier objective of deeplenstronomy is to help astronomers make their training datasets as realistic as possible. To that end, deeplenstronomy contains built-in features for the following functionalities: use any stellar light profile or mass profile in lenstronomy;simulate a variety of astronomical systems such as single galaxies, foreground stars, galaxy clusters, supernovae, and kilonovae, as well as any combination of those systems;fully control the placements of objects in the simulations;use observing conditions of real astronomical surveys;draw any parameter from any probability distribution;introduce any correlation;and incorporate real images into the simulation. Furthermore, deeplens tronomy facilitates realistic time-domain studies by providing access to public spectral energy distributions of observed supernovae and kilonovae and incorporating the transient objects into time series of simulated images. Finally, deeplenstronomy provides data visualization functions to enable users to inspect their simulation outputs. These features and the path from configuration file to full data set are shown in Figure 1. deeplenstronomy makes use of multiple open-source software packages: lenstronomy is used for all gravitational lensing calculations and image simulation;numpy (Harris et al. 2020) Arrays are used inter

关键词： Supernovae

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DeepZipper. II. Searching for Lensed Supernovae in Dark Energy survey data with Deep Learning

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ASTROPHYSICAL JOURNAL 2023年第1期943卷 19-19页

作者： Morgan, R. Nord, B. Bechtol, K. Moeller, A. Hartley, W. G. Birrer, S. Gonzalez, 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. da Costa, L. N. Costanzi, M. De Vicente, J. Desai, S. Doel, P. Everett, S. Ferrero, I Flaugher, B. Friedel, D. Frieman, J. Garcia-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-Chinchon, F. Pereira, M. E. S. Pieres, A. Plazas Malagon, A. A. 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. Univ Wisconsin Phys Dept Madison WI 53706 USA Fermilab Natl Accelerator Lab POB 500 Batavia IL 60510 USA Legacy Survey Space & Time Corp Data Sci Fellowship Program Menlo Pk CA 94025 USA Univ Chicago Kavli Inst Cosmol Phys Chicago IL 60637 USA Legacy Survey Space & Time 933 North Cherry Ave Tucson AZ 85721 USA Swinburne Univ Technol Ctr Astrophys & Supercomp Hawthorn Vic 3122 Australia Univ Geneva Dept Astron Ch Ecogia 16 CH-1290 Versoix Switzerland Stanford Univ Kavli Inst Particle Astrophys & Cosmol Dept Phys Stanford CA 94305 USA SLAC Natl Accelerator Lab Menlo Pk CA 94025 USA Natl Ctr Supercomp Applicat Ctr Astrophys Surveys 1205 West Clark St Urbana IL 61801 USA Univ Illinois Dept Astron 1002 W Green St Urbana IL 61801 USA Univ Chicago Dept Astron & Astrophys Chicago IL 60637 USA Inst Astrofis Canarias E-38205 Tenerife Spain Lab Interinst E Astron LIneA Rua Gal Jose Cristino 77 BR-20921400 Rio De Janeiro RJ Brazil Univ La Laguna Dept Astrofis E-38206 Tenerife Spain Australian Natl Univ Coll Sci Ctr Gravitat Astrophys Canberra ACT 2601 Australia Australian Natl Univ Res Sch Astron & Astrophys Canberra ACT 2601 Australia Univ Portsmouth Inst Cosmol & Gravitat Portsmouth PO1 3FX Hants England NSFs Natl Opt Infrared Astron Res Lab Cerro Tololo Interamer Observ Casilla 603 La Serena Chile Univ Michigan Dept Phys Ann Arbor MI 48109 USA Ludwig Maximilians Univ Munchen Fac Phys Univ Observ Scheinerstr 1 D-81679 Munich Germany UCL Dept Phys & Astron Gower St London WC1E 6BT England Stanford Univ Kavli Inst Particle Astrophys & Cosmol POB 2450 Stanford CA 94305 USA Barcelona Inst Sci & Technol Inst Fis Altes Energies IFAE Campus UAB E-08193 Bellaterra Barcelona Spain Inst Estudis Espacials Catalunya IEEC E-08034 Barcelona Spain CSIC Inst Space Sci ICE Campus UABCarrer Can Magrans S-N E-08193 Barcelona Spain Univ Manchester Jodrell Bank Ctr Astrophys Sch Phys & Astron Oxford Rd Manchester M13 9PL Lancs England Univ Notti

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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Expediting DECam Multimessenger Counterpart Searches with Convolutional Neural Networks

arXiv

引用

arXiv 2021年

作者： Shandonay, A. Morgan, Robert Bechtol, K. Bom, C.R. Nord, B. Garcia, A. Henghes, B. Herner, K. Tabbutt, M. Palmese, A. Santana-Silva, L. Soares-Santos, M. Gill, M.S.S. García-Bellido, J. Physics Department University of Wisconsin-Madison 1150 University Avenue MadisonWI53706 United States Legacy Survey of Space and Time Corporation Data Science Fellowship Program United States Legacy Survey of Space and Time 933 North Cherry Avenue TucsonAZ85721 United States Centro Brasileiro de Pesquisas Físicas Rua Dr. Xavier Sigaud 150 CEP Rio de Janeiro RJ 22290-180 Brazil Centro Federal de Educação Tecnológica Celso Suckow da Fonseca Rodovia Mário Covas Cep Itaguaí RJ 23810-000 Brazil Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Department of Astronomy University of Michigan Ann ArborMI48109 United States Department of Physics & Astronomy University College London Gower Street LondonWC1 E 6BT United Kingdom NAT-Universidade Cruzeiro do Sul Universidade Cidade de São Paulo Rua Galvão Bueno 868 SP São Paulo01506-000 Brazil Department of Physics University of Michigan Ann ArborMI48109 United States SLAC National Accelerator Laboratory Menlo ParkCA94025 United States Instituto de Fisica Teorica UAM CSIC Universidad Autonoma de Madrid Madrid28049 Spain

Searches for counterparts to multimessenger events with optical imagers use difference imaging to detect new transient sources. However, even with existing artifact detection algorithms, this process simultaneously returns several classes of false positives: false detections from poor quality image subtractions, false detections from low signal-to-noise images, and detections of pre-existing variable sources. Currently, human visual inspection to remove the false positives is a central part of multimessenger follow-up observations, but when next generation gravitational wave and neutrino detectors come online and increase the rate of multimessenger events, the visual inspection process will be prohibitively expensive. We approach this problem with two convolutional neural networks operating on the difference imaging outputs. The first network focuses on removing false detections and demonstrates an accuracy of 92 percent on our dataset. The second network focuses on sorting all real detections by the probability of being a transient source within a host galaxy and distinguishes between various classes of images that previously required additional human inspection. We find the number of images requiring human inspection will decrease by a factor of 1.5 using our approach alone and a factor of 3.6 using our approach in combination with existing algorithms, facilitating rapid multimessenger counterpart identification by the astronomical community. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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lenstronomy II: A gravitational lensing software ecosystem

arXiv

引用

arXiv 2021年

作者： Birrer, Simon Shajib, Anowar J. Gilman, Daniel Galan, Aymeric Aalbers, Jelle Millon, Martin Morgan, Robert Pagano, Giulia Park, Ji Won Teodori, Luca Tessore, Nicolas Ueland, Madison van de Vyvere, Lyne Wagner-Carena, Sebastian Wempe, Ewoud Yang, Lilan Ding, Xuheng Schmidt, Thomas Sluse, Dominique Zhang, Ming Amara, Adam Kavli Institute for Particle Astrophysics and Cosmology Department of Physics Stanford University StanfordCA94305 United States SLAC National Accelerator Laboratory Menlo ParkCA94025 United States Department of Astronomy & Astrophysics University of Chicago ChicagoIL60637 United States Department of Physics and Astronomy University of California Los AngelesCA90095 United States Department of Astronomy and Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada Switzerland Physics Department University of Wisconsin-Madison 1150 University Avenue MadisonWI53706 United States Legacy Survey of Space and Time Corporation Data Science Fellowship Program United States Weizmann Institute 234 Herzl Street Rehovot7610001 Israel Department of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom STAR Institute Université de Liège Quartier Agora - Allée du six Août 19c LiègeB-4000 Belgium Kapteyn Astronomical Institute University of Groningen PO Box 800 Groningen9700 AV Netherlands School of Physics and Technology Wuhan University Wuhan430072 China UTIAS University of Tokyo Kashiwa Chiba277-8583 Japan Xinjiang Astronomical Observatory Chinese Academy of Sciences 150 Science 1-Street Urumqi831001 China Institute of Cosmology and Gravitation University of Portsmouth PortsmouthPO1 3FX United Kingdom

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DeepZipper: A Novel Deep Learning Architecture for Lensed Supernovae Identification

arXiv

引用

arXiv 2021年

作者： Morgan, Robert Nord, B. Bechtol, K. González, S.J. Buckley-Geer, E. Möller, A. Park, J.W. Kim, A.G. Birrer, S. Aguena, M. Annis, J. Bocquet, S. Brooks, D. Rosell, A. Carnero Kind, M. Carrasco Carretero, J. Cawthon, R. da Costa, L.N. Davis, T.M. De Vicente, J. Doel, P. Ferrero, I. Friedel, D. Frieman, J. García-Bellido, J. Gatti, M. Gaztanaga, E. Giannini, G. Gruen, D. Gruendl, R.A. Gutierrez, G. Hollowood, D.L. Honscheid, K. James, D.J. Kuehn, K. Kuropatkin, N. Maia, M.A.G. Miquel, R. Palmese, A. Paz-Chinchón, F. Pereira, M.E.S. Pieres, A. Plazas Malagón, A.A. Reil, K. Roodman, A. Sanchez, E. Smith, M. Suchyta, E. Swanson, M.E.C. Tarle, G. To, C. Physics Department University of Wisconsin-Madison 1150 University Avenue MadisonWI53706 United States Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States Legacy Survey of Space and Time Corporation Data Science Fellowship Program United States Department of Astronomy and Astrophysics University of Chicago ChicagoIL60637 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Legacy Survey of Space and Time 933 North Cherry Avenue TucsonAZ85721 United States Centre for Astrophysics & Supercomputing Swinburne University of Technology VIC3122 Australia Kavli Institute for Particle Astrophysics and Cosmology Department of Physics Stanford University StanfordCA94305 United States SLAC National Accelerator Laboratory Menlo ParkCA94025 United States Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Laboratório Interinstitucional de e-Astronomia - LIneA Rua Gal. José Cristino 77 RJ Rio de Janeiro20921-400 Brazil Faculty of Physics Ludwig-Maximilians-Universität Scheinerstr. 1 Munich81679 Germany Department of Physics & Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Center for Astrophysical Surveys National Center for Supercomputing Applications 1205 West Clark St. UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign 1002 W. Green Street UrbanaIL61801 United States The Barcelona Institute of Science and Technology Campus UAB Bellaterra Barcelona08193 Spain Physics Department William Jewell College LibertyMO64068 United States Observatório Nacional Rua Gal. José Cristino 77 RJ Rio de Janeiro20921-400 Brazil School of Mathematics and Physics University of Queensland BrisbaneQLD4072 Australia Madrid Spain Institute of Theoretical Astrophysics University of Oslo P.O. Box 1029 Blindern OsloNO-0315 Norway Instituto de Fisica Teorica UAM/CSIC

Large-scale astronomical surveys have the potential to capture data on large numbers of strongly gravitationally lensed supernovae (LSNe). To facilitate timely analysis and spectroscopic follow-up before the supernova fades, an LSN needs to be identified soon after it begins. To quickly identify LSNe in optical survey datasets, we designed ZipperNet, a multi-branch deep neural network that combines convolutional layers (traditionally used for images) with long short-term memory (LSTM) layers (traditionally used for time series). We tested ZipperNet on the task of classifying objects from four categories – no lens, galaxy-galaxy lens, lensed type Ia supernova, lensed core-collapse supernova – within high-fidelity simulations of three cosmic survey data sets – the Dark Energy survey (DES), Rubin Observatory’s legacy survey of space and time (LSST), and a Dark Energy Spectroscopic Instrument (DESI) imaging survey. Among our results, we find that for the LSST-like dataset, ZipperNet classifies LSNe with a receiver operating characteristic area under the curve of 0.97, predicts the spectroscopic type of the lensed supernovae with 79% accuracy, and demonstrates similarly high performance for LSNe 1-2 epochs after first detection. We anticipate that a model like ZipperNet, which simultaneously incorporates spatial and temporal information, can play a significant role in the rapid identification of lensed transient systems in cosmic survey experiments. Copyright © 2021, The Authors. All rights reserved.

关键词： Supernovae

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DeepZipper II: Searching for Lensed Supernovae in Dark Energy survey data with Deep Learning

arXiv

引用

arXiv 2022年

作者： Morgan, Robert 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. Carnero Rosell, A. Lidman, C. Collett, T. Abbott, T.M.C. Aguena, M. Andrade-Oliveira, F. Annis, J. Bacon, D. Bocquet, S. Brooks, D. Burke, D.L. Carrasco Kind, M. Carretero, J. Castander, F.J. Conselice, C. da Costa, L.N. 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. Plazas Malagón, A.A. 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. Physics Department University of Wisconsin-Madison MadisonWI53706 United States Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States Legacy Survey of Space and Time Corporation Data Science Fellowship Program United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Legacy Survey of Space and Time 933 North Cherry Avenue TucsonAZ85721 United States Centre for Astrophysics & Supercomputing Swinburne University of Technology VIC3122 Australia Department of Astronomy University of Geneva ch. d'Ecogia 16 VersoixCH-1290 Switzerland Kavli Institute for Particle Astrophysics and Cosmology Department of Physics Stanford University StanfordCA94305 United States SLAC National Accelerator Laboratory Menlo ParkCA94025 United States Center for Astrophysical Surveys National Center for Supercomputing Applications 1205 West Clark St. UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign 1002 W. Green Street UrbanaIL61801 United States Department of Astronomy and Astrophysics University of Chicago ChicagoIL60637 United States Instituto de Astrofisica de Canarias Tenerife La LagunaE-38205 Spain Laboratório Interinstitucional de e-Astronomia - LIneA Rua Gal. José Cristino 77 RJ Rio de Janeiro20921-400 Brazil Universidad de La Laguna Dpto. Astrofísica Tenerife La LagunaE-38206 Spain Centre for Gravitational Astrophysics College of Science The Australian National University ACT2601 Australia The Research School of Astronomy and Astrophysics The Australian National University ACT2601 Australia Institute of Cosmology and Gravitation University of Portsmouth PortsmouthPO1 3FX United Kingdom Cerro Tololo Inter-American Observatory NSF's National Optical-Infrared Astronomy Research Laboratory Casilla 603 La Serena Chile Department of Physics University of Michigan Ann ArborMI48109 United States University Observatory Faculty of Ph

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 hundreds to a few thousands of these systems. We search for these systems in observed Dark Energy survey (DES) 5-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 a LSN recall of 61.13% and a false positive rate of 0.02% on the DES SN field data. DeepZipper selected 2,245 candidates from a magnitude-limited (mi Copyright © 2022, The Authors. All rights reserved.

关键词： Supernovae

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Designing an Optimal Kilonova Search Using DECam for Gravitational-wave Events

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ASTROPHYSICAL JOURNAL 2024年第2期960卷 122页

作者： Bom, C. R. Annis, J. Garcia, A. Palmese, A. Sherman, N. Soares-Santos, M. Santana-Silva, L. Morgan, R. Bechtol, K. Davis, T. Diehl, H. T. Allam, S. S. Bachmann, T. G. Fraga, B. M. O. Garcia-Bellido, J. Gill, M. S. S. Herner, K. Kilpatrick, C. D. Makler, M. Olivares E., F. Pereira, M. E. S. Pineda, J. Santos, A. Tucker, D. L. Wiesner, M. P. Aguena, M. Alves, O. Bacon, D. Bernardinelli, P. H. Bertin, E. Bocquet, S. Brooks, D. Carrasco Kind, M. Carretero, J. Conselice, C. Costanzi, M. da Costa, L. N. De Vicente, J. Desai, S. Doel, P. Everett, S. Ferrero, I. Frieman, J. Gatti, M. Gerdes, D. W. Gruen, D. Gruendl, R. A. Gutierrez, G. Hinton, S. R. Hollowood, D. L. Honscheid, K. James, D. J. Kuehn, K. Kuropatkin, N. Melchior, P. Mena-Fernandez, J. Menanteau, F. Pieres, A. Plazas Malagon, A. A. Raveri, M. Rodriguez-Monroy, M. Sanchez, E. Santiago, B. Sevilla-Noarbe, I. Smith, M. Suchyta, E. Swanson, M. E. C. Tarle, G. To, C. Weaverdyck, N. Ctr Brasileiro Pesquisas Fis Rua Dr Xavier Sigaud 150 BR-22290180 Rio De Janeiro RJ Brazil Ctr Fed Educ Tecnol Celso Suckow Fonseca Rodovia Mario CovasLote J2Quadra J BR-23810000 Itaguai RJ Brazil Fermilab Natl Accelerator Lab POB 500 Batavia IL 60510 USA Univ Michigan Dept Phys Ann Arbor MI 48109 USA Univ Calif Berkeley Dept Phys 366 LeConte HallMC 7300 Berkeley CA 94720 USA Univ Cidade Sao Paulo Univ Cruzeiro Sul NAT Rua Galvao Bueno 868 BR-01506000 Sao Paulo SP Brazil Univ Wisconsin Madison Dept Phys 2320 Chamberlin Hall1150 Univ Ave Madison WI 53706 USA Univ Queensland Sch Math & Phys Brisbane Qld 4072 Australia Univ Chicago Dept Astron & Astrophys Chicago IL 60637 USA Univ Autonoma Madrid Inst Fis Teor UAM CSIC E-28049 Madrid Spain SLAC Natl Accelerator Lab Menlo Pk CA 94025 USA Northwestern Univ Ctr Interdisciplinary Explorat & Res Astrophys CI Evanston IL 60208 USA Northwestern Univ Dept Phys & Astron Evanston IL 60208 USA ECyT UNSAM Int Ctr Adv Studies RA-1650 Buenos Aires DF Argentina ECyT UNSAM Inst Ciencias Fis RA-1650 Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn RA-1650 Buenos Aires DF Argentina Univ Atacama Inst Astron & Ciencias Planetarias Ave Copayapu 485 Copiapo Chile Univ Hamburg Hamburger Sternwarte Gojenbergsweg 112 D-21029 Hamburg Germany Univ Andres Bello Dept Ciencias Fis Avda Republ 252 Santiago Chile Millennium Inst Astrophys Nuncio Monsenor Sotero Sanz 100 Santiago Chile Benedictine Univ Dept Phys 5700 Coll Rd Lisle IL 60532 USA Lab Interinst Eastron LIneA Rua Gal Jose Cristino 77 BR-20921400 Rio De Janeiro RJ Brazil Univ Portsmouth Inst Cosmol & Gravitat Portsmouth PO1 3FX Hants England Univ Washington Dept Astron Box 351580 Seattle WA 98195 USA Inst Astrophys Paris CNRS UMR 7095 F-75014 Paris France UPMC Univ Paris 06 Sorbonne Univ UMR 7095 Inst Astrophys Paris F-75014 Paris France Ludwig Maximilians Univ Munchen Fac Phys Univ Observ Scheinerstr 1 D-81679 Munich Germ

We address the problem of optimally identifying all kilonovae detected via gravitational-wave emission in the upcoming LIGO/Virgo/KAGRA observing run, O4, which is expected to be sensitive to a factor of similar to 7 more binary neutron star (BNS) alerts than previously. Electromagnetic follow-up of all but the brightest of these new events will require >1 m telescopes, for which limited time is available. We present an optimized observing strategy for the DECam during O4. We base our study on simulations of gravitational-wave events expected for O4 and wide-prior kilonova simulations. We derive the detectabilities of events for realistic observing conditions. We optimize our strategy for confirming a kilonova while minimizing telescope time. For a wide range of kilonova parameters, corresponding to a fainter kilonova compared to GW170817/AT 2017gfo, we find that, with this optimal strategy, the discovery probability for electromagnetic counterparts with the DECam is similar to 80% at the nominal BNS gravitational-wave detection limit for O4 (190 Mpc), which corresponds to an similar to 30% improvement compared to the strategy adopted during the previous observing run. For more distant events (similar to 330 Mpc), we reach an similar to 60% probability of detection, a factor of similar to 2 increase. For a brighter kilonova model dominated by the blue component that reproduces the observations of GW170817/AT 2017gfo, we find that we can reach similar to 90% probability of detection out to 330 Mpc, representing an increase of similar to 20%, while also reducing the total telescope time required to follow up events by similar to 20%.

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Designing an Optimal Kilonova Search using DECam for Gravitational Wave Events

arXiv

引用

arXiv 2023年

作者： Bom, C.R. Annis, J. Garcia, A. Palmese, A. Sherman, N. Soares-Santos, M. Santana-Silva, L. Morgan, R. Bechtol, K. Davis, T. Diehl, H.T. Allam, S.S. Bachmann, T.G. Fraga, B.M.O. García-Bellido, J. Gill, M.S.S. Herner, K. Kilpatrick, C.D. Makler, M. Olivares, F.E. Pereira, M.E.S. Pineda, J. Santos, A. Tucker, D.L. Wiesner, M.P. Aguena, M. Alves, O. Bacon, D. Bernardinelli, P.H. Bertin, E. Bocquet, S. Brooks, D. Kind, M. Carrasco Carretero, J. Conselice, C. Costanzi, M. da Costa, L.N. De Vicente, J. Desai, S. Doel, P. Everett, S. Ferrero, I. Frieman, J. Gatti, M. Gerdes, D.W. Gruen, D. Gruendl, R.A. Gutierrez, G. Hinton, S.R. Hollowood, D.L. Honscheid, K. James, D.J. Kuehn, K. Kuropatkin, N. Melchior, P. Mena-Fernández, J. Menanteau, F. Pieres, A. Malagón, A.A. Plazas Raveri, M. Rodriguez-Monroy, M. Sanchez, E. Santiago, B. Sevilla-Noarbe, I. Smith, M. Suchyta, E. Swanson, M.E.C. Tarle, G. To, C. Weaverdyck, N. Centro Brasileiro de Pesquisas Físicas Rua Dr. Xavier Sigaud 150 RJ Rio de JaneiroCEP 22290-180 Brazil Centro Federal de Educação Tecnológica Celso Suckow da Fonseca Rodovia Mário Covas lote J2 quadra JItaguaí RJ CEP 23810-000 Brazil Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States Department of Physics University of Michigan Ann ArborMI48109 United States Department of Physics University of California Berkeley 366 LeConte Hall MC 7300 BerkeleyCA94720 United States NAT-Universidade Cruzeiro do Sul Universidade Cidade de São Paulo Rua Galvão Bueno 868 SP São Paulo01506-000 Brazil Physics Department 2320 Chamberlin Hall University of Wisconsin-Madison 1150 University Avenue MadisonWI53706-1390 United States Legacy Survey of Space and Time Corporation Data Science Fellowship Program School of Mathematics and Physics The University of Queensland BrisbaneQLD4072 Australia Department of Astronomy and Astrophysics University of Chicago ChicagoIL60637 United States Instituto de Fisica Teorica UAM CSIC Universidad Autonoma de Madrid Madrid28049 Spain SLAC National Accelerator Laboratory Menlo ParkCA94025 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States International Center for Advanced Studies Instituto de Ciencias Físicas ECyT-UNSAM & CONICET Buenos Aires1650 Argentina Instituto de Astronomía y Ciencias Planetarias Universidad de Atacama Av. Copayapu 485 Copiapó Chile Hamburger Sternwarte Universität Hamburg Gojenbergsweg 112 Hamburg21029 Germany Departamento de Ciencias Fisicas Universidad Andres Bello Avda. Republica 252 Santiago Chile Nuncio Monseñor Sótero Sanz 100 Providencia Santiago Chile Benedictine University Department of Physics 5700 College Road LisleIL60532 United States Laboratório Interinstitucional de e-Astronomia - LIneA Rua Gal. José Cristino 77 RJ Rio de Janeiro20921-400 Brazil Institute of Cosmology and Gravitation

We address the problem of optimally identifying all kilonovae detected via gravitational wave emission in the upcoming LIGO/Virgo/KAGRA observing run, O4, which is expected to be sensitive to a factor of ∼ 7 more Binary Neutron Stars (BNS) alerts than previously. Electromagnetic follow-up of all but the brightest of these new events will require > 1 meter telescopes, for which limited time is available. We present an optimized observing strategy for the DECam during O4. We base our study on simulations of gravitational wave events expected for O4 and wide-prior kilonova simulations. We derive the detectabilities of events for realistic observing conditions. We optimize our strategy for confirming a kilonova while minimizing telescope time. For a wide range of kilonova parameters, corresponding to a fainter kilonova compared to GW170817/AT2017gfo we find that, with this optimal strategy, the discovery probability for electromagnetic counterparts with the DECam is ∼ 80% at the nominal BNS gravitational wave detection limit for O4 (190 Mpc), which corresponds to a ∼ 30% improvement compared to the strategy adopted during the previous observing run. For more distant events (∼ 330 Mpc), we reach a ∼ 60% probability of detection, a factor of ∼ 2 increase. For a brighter kilonova model dominated by the blue component that reproduces the observations of GW170817/AT2017gfo, we find that we can reach ∼ 90% probability of detection out to 330 Mpc, representing an increase of ∼ 20%, while also reducing the total telescope time required to follow-up events by ∼ 20%. Copyright © 2023, The Authors. All rights reserved.

关键词： Gravity waves

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SOAR/Goodman Spectroscopic Assessment of Candidate Counterparts of the LIGO-Virgo Event GW190814

arXiv

引用

arXiv 2021年

作者： Tucker, Douglas L. Wiesner, M.P. Allam, S.S. Soares-Santos, M. Bom, C.R. Butner, M. Garcia, A. Morgan, R. Olivares E., F. Palmese, A. Santana-Silva, L. Shrivastava, A. Annis, J. García-Bellido, J. Gill, M.S.S. Herner, K. Kilpatrick, C.D. Makler, M. Sherman, N. Amara, A. Lin, H. Smith, M. Swann, E. Arcavi, I. Bachmann, T.G. Bechtol, K. Berlfein, F. Briceno, C. Brout, D. Butler, R.E. Cartier, R. Casares, J. Chen, H.-Y. Conselice, C. Contreras, C. Cook, E. Cooke, J. Dage, K. D'Andrea, C. Davis, T.M. De Carvalho, R. Diehl, H.T. Dietrich, J.P. Doctor, Z. Drlica-Wagner, A. Drout, M. Farr, B. Finley, D.A. Fishbach, M. Foley, R.J. Förster-Burón, F. Fosalba, P. Friedel, D. Frieman, J. Frohmaier, C. Gruendl, R.A. Hartley, W.G. Hiramatsu, D. Holz, D.E. Howell, D.A. Kawash, A. Kessler, R. Kuropatkin, N. Lahav, O. Lundgren, A. Lundquist, M. Malik, U. Mann, A.W. Marriner, J. Marshall, J.L. Martínez-Vázquez, C.E. McCully, C. Menanteau, F. Meza, N. Narayan, G. Neilsen, E. Nicolaou, C. Nichol, R. Paz-Chinchón, F. Pereira, M.E.S. Pineda, J. Points, S. Quirola-Vásquez, J. Rembold, S. Rest, A. Rodriguez, O. Romer, A.K. Sako, M. Salim, S. Scolnic, D. Smith, J.A. Strader, J. Sullivan, M. Swanson, M.E.C. Thomas, D. Valenti, S. Varga, T.N. Walker, A.R. Weller, J. Wood, M.L. Yanny, B. Zenteno, A. Aguena, M. Andrade-Oliveira, F. Bertin, E. Brooks, D. Burke, D.L. Carnero Rosell, A. Carrasco Kind, M. Carretero, J. Costanzi, M. Da Costa, L.N. De Vicente, J. Desai, S. Everett, S. Ferrero, I. Flaugher, B. Gaztanaga, E. Gerdes, D.W. Gruen, D. Gschwend, J. Gutierrez, G. Hinton, S.R. Hollowood, D.L. Honscheid, K. James, D.J. Kuehn, K. Lima, M. Maia, M.A.G. Miquel, R. Ogando, R.L.C. Pieres, A. Plazas Malagón, A.A. Rodriguez-Monroy, M. Sanchez, E. Scarpine, V. Schubnell, M. Serrano, S. Sevilla-Noarbe, I. Suchyta, E. Tarle, G. To, C. Zhang, Y. Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States Benedictine University Department of Physics 5700 College Road LisleIL60532 United States Department of Physics University of Michigan Ann ArborMI48109 United States Centro Brasileiro de Pesquisas Físicas Rua Dr. Xavier Sigaud 150 RJ Rio de JaneiroCEP 22290-180 Brazil Centro Federal de Educação Tecnológica Celso Suckow da Fonseca Rodovia Mário Covas lote J2 quadra J Itaguaí RJ CEP 23810-000 Brazil East Tennessee State University 1276 Gilbreath Dr. Box 70300 Johnson CityTN37614 United States Physics Department University of Wisconsin-Madison 1150 University Avenue Madison 2320 Chamberlin Hall WI53706-1390 United States Legacy Survey of Space Time Corporation Data Science Fellowship Program Instituto de Astronomía y Ciencias Planetarias Universidad de Atacama Copayapu 485 Copiapó Chile Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States NAT-Universidade Cruzeiro do Sul Universidade Cidade de São Paulo Rua Galvão Bueno 868 SP São Paulo01506-000 Brazil Instituto de Fisica Teorica UAM CSIC Universidad Autonoma de Madrid Madrid28049 Spain SLAC National Accelerator Laboratory Menlo ParkCA94025 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States International Center for Advanced Studies Instituto de Ciencias Físicas ECyT-UNSAM CONICET Buenos Aires1650 Argentina Institute of Cosmology and Gravitation University of Portsmouth PortsmouthPO1 3FX United Kingdom School of Physics and Astronomy University of Southampton SouthamptonSO17 1BJ United Kingdom The School of Physics and Astronomy Tel Aviv University Tel Aviv69978 Israel CIFAR Azrieli Global Scholars program CIFAR ON Toronto Canada Department of Astronomy and Astrophysics University of Chicago ChicagoIL60637 United States Department of Physics Stanford University 382 Via Pueblo Mall Stanford CA94

On 2019 August 14 at 21:10:39 UTC, the LIGO/Virgo Collaboration (LVC) detected a possible neutron star-black hole merger (NSBH), the first ever identified. An extensive search for an optical counterpart of this event, designated GW190814, was undertaken using the Dark Energy Camera (DECam) on the 4m Victor M. Blanco Telescope at the Cerro Tololo Inter-American Observatory. Target of Opportunity interrupts were issued on 8 separate nights to observe 11 candidates using the 4.1m Southern Astrophysical Research (SOAR) telescope's Goodman High Throughput Spectrograph in order to assess whether any of these transients was likely to be an optical counterpart of the possible NSBH merger. Here, we describe the process of observing with SOAR, the analysis of our spectra, our spectroscopic typing methodology, and our resultant conclusion that none of the candidates corresponded to the gravitational wave merger event but were all instead other transients. Finally, we describe the lessons learned from this effort. Application of these lessons will be critical for a successful community spectroscopic follow-up program for LVC observing run 4 (O4) and beyond. © 2021, CC BY.

关键词： Gravity waves

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