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

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.
0035-8711
5330-5349
Smith, Mathew
8bdc74e1-a37b-434d-ae75-82763109bf7a
DES Collaboration
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), 5330-5349. (doi:10.1093/mnras/stz272).

Record type: Article

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.

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Finding high-redshift strong lenses in DES using convolutional neural networks - Accepted Manuscript
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More information

Accepted/In Press date: 22 January 2019
e-pub ahead of print date: 25 January 2019
Additional Information: AM attached

Identifiers

Local EPrints ID: 430326
URI: http://eprints.soton.ac.uk/id/eprint/430326
ISSN: 0035-8711
PURE UUID: 18b6c6fd-e756-4548-b61d-ef7091602575
ORCID for Mathew Smith: ORCID iD orcid.org/0000-0002-3321-1432

Catalogue record

Date deposited: 25 Apr 2019 16:30
Last modified: 26 Nov 2021 03:03

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Contributors

Author: Mathew Smith ORCID iD
Corporate Author: DES Collaboration

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