Finding high-redshift strong lenses in DES using convolutional neural networks
Finding high-redshift strong lenses in DES using convolutional neural networks
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.
5330-5349
Smith, Mathew
8bdc74e1-a37b-434d-ae75-82763109bf7a
Smith, Mathew
8bdc74e1-a37b-434d-ae75-82763109bf7a
Smith, Mathew
,
DES Collaboration
(2019)
Finding high-redshift strong lenses in DES using convolutional neural networks.
Monthly Notices of the Royal Astronomical Society, 484 (4), .
(doi:10.1093/mnras/stz272).
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 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.
Text
Finding high-redshift strong lenses in DES using convolutional neural networks
- Accepted Manuscript
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Accepted/In Press date: 22 January 2019
e-pub ahead of print date: 25 January 2019
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Local EPrints ID: 430326
URI: http://eprints.soton.ac.uk/id/eprint/430326
ISSN: 1365-2966
PURE UUID: 18b6c6fd-e756-4548-b61d-ef7091602575
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Date deposited: 25 Apr 2019 16:30
Last modified: 16 Mar 2024 04:19
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Corporate Author: DES Collaboration
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