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

  • Author(s): Jacobs, C
  • Collett, T
  • Glazebrook, K
  • McCarthy, C
  • Qin, AK
  • Abbott, TMC
  • Abdalla, FB
  • Annis, J
  • Avila, S
  • Bechtol, K
  • Bertin, E
  • Brooks, D
  • Buckley-Geer, E
  • Burke, DL
  • Carnero Rosell, A
  • Carrasco Kind, M
  • Carretero, J
  • da Costa, LN
  • Davis, C
  • De Vicente, J
  • Desai, S
  • Diehl, HT
  • Doel, P
  • Eifler, TF
  • Flaugher, B
  • Frieman, J
  • García-Bellido, J
  • Gaztanaga, E
  • Gerdes, DW
  • Goldstein, DA
  • Gruen, D
  • Gruendl, RA
  • Gschwend, J
  • Gutierrez, G
  • Hartley, WG
  • Hollowood, DL
  • Honscheid, K
  • Hoyle, B
  • James, DJ
  • Kuehn, K
  • Kuropatkin, N
  • Lahav, O
  • Li, TS
  • Lima, M
  • Lin, H
  • Maia, MAG
  • Martini, P
  • Miller, CJ
  • Miquel, R
  • Nord, B
  • Plazas, AA
  • Sanchez, E
  • Scarpine, V
  • Schubnell, M
  • Serrano, S
  • Sevilla-Noarbe, I
  • Smith, M
  • Soares-Santos, M
  • Sobreira, F
  • Suchyta, E
  • Swanson, MEC
  • Tarle, G
  • Vikram, V
  • Walker, AR
  • Zhang, Y
  • Zuntz, J
  • et al.

Published Web Location

https://arxiv.org/abs/1811.03786v2
No data is associated with this publication.
Abstract

© 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. 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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