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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. García- Bellido
; E. Gaztanaga
; D. W. Gerdes
; D. A. Goldstein
; D. Gruen
; R. 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
; | | Date: |
9 Nov 2018 | | Abstract: | 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 7,301 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
9,428 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. Based on simulations we estimate our
sample to contain most discoverable lenses in this imaging and at this redshift
range. | | Source: | arXiv, 1811.3786 | | Services: | Forum | Review | PDF | Favorites | |
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