Finding high-redshift strong lenses in DES using convolutional neural networks

C. Jacobs; T. Collett; K. Glazebrook; C. McCarthy; A. K. Qin; T. M. C. Abbott; F. B. Abdalla; J. Annis; S. Avila; K. Bechtol; E. Bertin; D. Brooks; E. Buckley-Geer; D. L. Burke; A. Carnero Rosell; M. Carrasco Kind; J. Carretero; L. N. da Costa; C. Davis; J. De Vicente; S. Desai; H. T. Diehl; P. Doel; T. F. Eifler; B. Flaugher; J. Frieman; J. Garcia-Bellido; E. Gaztanaga; D. W. Gerdes; D. A. Goldstein; D. Gruen; A. Gruendl; J. Gschwend; G. Gutierrez; W. G. Hartley; D. L. Hollowood; K. Honscheid; B. Hoyle; D. J. James; K. Kuehn; N. Kuropatkin; O. Lahav; T. S. Li; M. Lima; H. Lin; M. A. G. Maia; P. Martini; C. J. Miller; R. Miquel; B. Nord; A. A. Plazas; E. Sanchez; V. Scarpine; M. Schubnell; S. Serrano; I. Sevilla-Noarbe; M. Smith; M. Soares-Santos; F. Sobreira; E. Suchyta; M. E. C. Swanson; G. Tarle; V. Vikram; A. R. Walker; Y. Zhang; J. Zuntz; DES Collaboration

doi:10.1093/mnras/stz272

Back

Finding high-redshift strong lenses in DES using convolutional neural networks

Journal article

Peer reviewed

Finding high-redshift strong lenses in DES using convolutional neural networks

C. Jacobs, T. Collett, K. Glazebrook, C. McCarthy, A. K. Qin, T. M. C. Abbott, F. B. Abdalla, J. Annis, S. Avila, K. Bechtol, …

Monthly notices of the Royal Astronomical Society, Vol.484(4), pp.5330-5349

04/21/2019

DOI: https://doi.org/10.1093/mnras/stz272

Abstract

Astronomy & Astrophysics

Science & Technology

Physical Sciences

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.

Metrics

1 Record Views

See more details

Details

Title: Finding high-redshift strong lenses in DES using convolutional neural networks
Creators: C. Jacobs - Swinburne University of Technology
T. Collett - University of Portsmouth
K. Glazebrook - Swinburne University of Technology
C. McCarthy - Swinburne University of Technology
A. K. Qin - Swinburne University of Technology
T. M. C. Abbott - Cerro Tololo Inter-American Observatory
F. B. Abdalla - Rhodes University
J. Annis - Fermi National Accelerator Laboratory
S. Avila - University of Portsmouth
K. Bechtol - University of Wisconsin–Madison
E. Bertin - Centre National de la Recherche Scientifique
D. Brooks - University College London
E. Buckley-Geer - Fermi National Accelerator Laboratory
D. L. Burke - SLAC National Accelerator Laboratory
A. Carnero Rosell - Laboratório Interinstitucional de e-Astronomia
M. Carrasco Kind - University of Illinois Urbana-Champaign
J. Carretero - Institute for High Energy Physics
L. N. da Costa - Laboratório Interinstitucional de e-Astronomia
C. Davis - University of Louisville
J. De Vicente - Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas
S. Desai - Indian Institute of Technology Hyderabad
H. T. Diehl - Fermi National Accelerator Laboratory
P. Doel - University College London
T. F. Eifler - Jet Propulsion Laboratory
B. Flaugher - Fermi National Accelerator Laboratory
J. Frieman - Fermi National Accelerator Laboratory
J. Garcia-Bellido - Universidad Autónoma de Madrid
E. Gaztanaga - Institut d'Estudis Espacials de Catalunya
D. W. Gerdes - University of Michigan
D. A. Goldstein - California Institute of Technology
D. Gruen - Kavli Institute for Particle Astrophysics and Cosmology
A. Gruendl - University of Illinois Urbana-Champaign
J. Gschwend - Laboratório Interinstitucional de e-Astronomia
G. Gutierrez - Fermi National Accelerator Laboratory
W. G. Hartley - ETH Zurich
D. L. Hollowood - Santa Cruz Inst Particle Phys, Santa Cruz, CA 95064 USA
K. Honscheid - The Ohio State University
B. Hoyle - Max Planck Institute for Extraterrestrial Physics
D. J. James - Center for Astrophysics Harvard & Smithsonian
K. Kuehn - Lowell Observatory
N. Kuropatkin - Fermi National Accelerator Laboratory
O. Lahav - University College London
T. S. Li - Fermi National Accelerator Laboratory
M. Lima - Laboratório Interinstitucional de e-Astronomia
H. Lin - Fermi National Accelerator Laboratory
M. A. G. Maia - Laboratório Interinstitucional de e-Astronomia
P. Martini - The Ohio State University
C. J. Miller - University of Michigan
R. Miquel - Institució Catalana de Recerca i Estudis Avançats
B. Nord - Fermi National Accelerator Laboratory
A. A. Plazas - Jet Propulsion Laboratory
E. Sanchez - Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas
V. Scarpine - Fermi National Accelerator Laboratory
M. Schubnell - University of Michigan
S. Serrano - Institute of Space Sciences
I. Sevilla-Noarbe - Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas
M. Smith - University of Southampton
M. Soares-Santos - Brandeis University, Martin A. Fisher School of Physics
F. Sobreira - Universidade Estadual de Campinas (UNICAMP)
E. Suchyta - Oak Ridge National Laboratory
M. E. C. Swanson - National Center for Supercomputing Applications
G. Tarle - Institute for High Energy Physics
V. Vikram - Argonne National Laboratory
A. R. Walker - Cerro Tololo Inter-American Observatory
Y. Zhang - Fermi National Accelerator Laboratory
J. Zuntz - UK Astronomy Technology Centre
DES Collaboration
Publication Details: Monthly notices of the Royal Astronomical Society, Vol.484(4), pp.5330-5349
Publisher: Oxford Univ Press
Number of pages: 20
Grant note: CERCA programme of the Generalitat de Catalunya University of Edinburgh University of Chicago Kavli Institute of Cosmological Physics at the University of Chicago Lawrence Berkeley National Laboratory; United States Department of Energy (DOE) Ministerio da Ciencia, Tecnologia e Inovacao; Spanish Government Ministry of Science and Education of Spain; Spanish Government Institut de Ciencies de l'Espai (IEEC/CSIC) Ohio State University University of Pennsylvania
Identifiers: 9924552843401921
Academic Unit: Martin A. Fisher School of Physics
Language: English
Resource Type: Journal article

Finding high-redshift strong lenses in DES using convolutional neural networks

Abstract

Metrics

Details

Brandeis University Social media