Jacobs, C;
Collett, T;
Glazebrook, K;
McCarthy, C;
Qin, AK;
Abbott, TMC;
Abdalla, FB;
... Zuntz, J; + view all
(2019)
Finding high-redshift strong lenses in DES using convolutional neural networks.
Monthly Notices of the Royal Astronomical Society
, 484
(4)
pp. 5330-5349.
10.1093/mnras/stz272.
Preview |
Text
stz272.pdf - Published Version Download (18MB) | Preview |
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.
| Type: | Article |
|---|---|
| Title: | Finding high-redshift strong lenses in DES using convolutional neural networks |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/mnras/stz272 |
| Publisher version: | https://doi.org/10.1093/mnras/stz272 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Physical Sciences, Astronomy & Astrophysics, gravitational lensing: strong, methods: statistical, STRONG GRAVITATIONAL LENSES, STAR-FORMING GALAXY, SPACE WARPS, SAMPLE, ARCS, HALO, II. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10072346 |
Archive Staff Only
 |
View Item |
