Huang, X. and Domingo, M. and Pilon, A. and Ravi, V. and Storfer, C. and Schlegel, D. J. and Bailey, S. and Dey, A. and Herrera, D. and Juneau, S. and Landriau, M. and Lang, D. and Meisner, A. and Moustakas, J. and Myers, A. D. and Schlafly, E. F. and Valdes, F. and Weaver, B. A. and Yang, J. and Yèche, C. (2019) Finding Strong Gravitational Lenses in the DESI DECam Legacy Survey. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20190624-144237321
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Abstract
We perform a semi-automated search for strong gravitational lensing systems in the 9,000 deg^2 Dark Energy Camera Legacy Survey (DECaLS), part of the DESI Legacy Imaging Surveys (Dey et al.). The combination of the depth and breadth of these surveys are unparalleled at this time, making them particularly suitable for discovering new strong gravitational lensing systems. We adopt the deep residual neural network architecture (He et al.) developed by Lanusse et al. for the purpose of finding strong lenses in photometric surveys. We compile a training set that consists of known lensing systems in the Legacy Surveys and DES as well as non-lenses in the footprint of DECaLS. In this paper we show the results of applying our trained neural network to the cutout images centered on galaxies typed as ellipticals (Lang et al.) in DECaLS. The images that receive the highest scores (probabilities) are visually inspected and ranked. Here we present 335 candidate strong lensing systems, identified for the first time.
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| Additional Information: | We thank Steve Farrell, Mustafa Mustafa, Laurie Stephey, and Rollin Thomas at the National Energy Scientific Computating Center (NERSC) for their consultation and advice. We thank Greg Aldering for insightful conversations in compiling our training sample and Ravi Gupta for helpful discussions regarding the lensing candidates. We are grateful to Joel Brownstein and Lexi Moustakas for granting us access to the Master Lens Database (http://admin.masterlens.org/index.php). This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02- 05CH11231 and the Computational HEP program in The Department of Energy's Science Office of High Energy Physics provided resources through the “Cosmology Data Repository" project (Grant #KA2401022). X.H. acknowledges the University of San Francisco Faculty Development Fund. A.D.'s research is supported by the National Optical Astronomy Observatory, which is operated by the Association of Universities for Research in Astronomy (AURA) under cooperative agreement with the National Science Foundation. This paper is based on observations at Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory (NOAO Prop. ID: 2014B-0404; co-PIs: D. J. Schlegel and A. Dey), which is operated by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. This project used data obtained with the Dark Energy Camera (DECam), which was constructed by the Dark Energy Survey (DES) collaboration. Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnólogico and the Ministério da Ciência, Tecnologia e Inovacão, the Deutsche Forschungsgemeinschaft, and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Enérgeticas, Medioambientales y Tecnológicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenössische Technische Hochschule (ETH) Zürich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ciències de l'Espai (IEEC/CSIC), the Institut de Física d'Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universität München and the associated Excellence Cluster Universe, the University of Michigan, the National Optical Astronomy Observatory, the University of Nottingham, the Ohio State University, the OzDES Membership Consortium the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, and Texas A&M University. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Subject Keywords: | galaxies: high-redshift - gravitational lensing: strong | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Record Number: | CaltechAUTHORS:20190624-144237321 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20190624-144237321 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| ID Code: | 96674 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Collection: | CaltechAUTHORS | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Deposited By: | Tony Diaz | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Deposited On: | 25 Jun 2019 18:02 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Last Modified: | 16 Nov 2019 05:50 |
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