期刊
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
卷 484, 期 4, 页码 5330-5349出版社
OXFORD UNIV PRESS
DOI: 10.1093/mnras/stz272
关键词
gravitational lensing: strong; methods: statistical
资金
- Australian Research Council Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D) [CE170100013]
- University of Portsmouth
- US Department of Energy
- US National Science Foundation
- Ministry of Science and Education of Spain
- Science and Technology Facilities Council of the United Kingdom
- Higher Education Funding Council for England
- National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign
- Kavli Institute of Cosmological Physics at the University of Chicago
- Center for Cosmology and Astro-Particle Physics at the Ohio State University
- Mitchell Institute for Fundamental Physics and Astronomy at Texas AM University
- Financiadora de Estudos e Projetos
- Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico
- Ministerio da Ciencia, Tecnologia e Inovacao
- Deutsche Forschungsgemeinschaft
- Argonne National Laboratory
- University of California at Santa Cruz
- University of Cambridge
- Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas-Madrid
- University of Chicago
- Eidgenossische Technische Hochschule (ETH) Zurich
- Fermi National Accelerator Laboratory
- University of Illinois at Urbana-Champaign
- Institut de Ciencies de l'Espai (IEEC/CSIC)
- Institut de Fisica d'Altes Energies
- Lawrence Berkeley National Laboratory
- Ludwig-Maximilians Universitat Munchen
- associated Excellence Cluster Universe
- University of Michigan
- National Optical Astronomy Observatory
- University of Nottingham
- Ohio State University
- University of Pennsylvania
- SLAC National Accelerator Laboratory
- Stanford University
- University of Sussex
- Texas AM University
- OzDES Membership Consortium
- National Science Foundation [AST-1138766, AST-1536171]
- MINECO [AYA2015-71825, ESP2015-66861, FPA2015-68048, SEV-2016-0588, SEV-2016-0597, MDM-2015-0509]
- ERDF funds from the European Union
- CERCA programme of the Generalitat de Catalunya
- European Research Council under the European Union's Seventh Framework Program (FP7/2007-2013) [240672, 291329, 306478]
- Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO) [CE110001020]
- Brazilian Instituto Nacional de Ciencia e Tecnologia (INCT) e-Universe (CNPq) [465376/2014-2]
- US Department of Energy, Office of Science, Office of High Energy Physics [DE-AC02-07CH11359]
- University College London
- DES-Brazil Consortium
- University of Edinburgh
- STFC [ST/R000972/1, ST/M001334/1] Funding Source: UKRI
We search Dark Energy Survey (DES) Year 3 imaging data for galaxy-galaxy strong gravitational lenses using convolutional neural networks. We generate 250 000 simulated lenses at redshifts > 0.8 from which we create a data set for training the neural networks with realistic seeing, sky and shot noise. Using the simulations as a guide, we build a catalogue of 1.1 million DES sources with 1.8 < g - i < 5, 0.6 < g - r < 3, r_mag > 19, g_mag > 20, and i_mag > 18.2. We train two ensembles of neural networks on training sets consisting of simulated lenses, simulated non-lenses, and real sources. We use the neural networks to score images of each of the sources in our catalogue with a value from 0 to 1, and select those with scores greater than a chosen threshold for visual inspection, resulting in a candidate set of 7301 galaxies. During visual inspection, we rate 84 as 'probably' or 'definitely' lenses. Four of these are previously known lenses or lens candidates. We inspect a further 9428 candidates with a different score threshold, and identify four new candidates. We present 84 new strong lens candidates, selected after a few hours of visual inspection by astronomers. This catalogue contains a comparable number of high-redshift lenses to that predicted by simulations. Based on simulations, we estimate our sample to contain most discoverable lenses in this imaging and at this redshift range.
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