Zaborowski, EA;
Drlica-Wagner, A;
Ashmead, F;
Wu, JF;
Morgan, R;
Bom, CR;
Shajib, AJ;
... Weaverdyck, N; + view all
(2023)
Identification of Galaxy-Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning.
The Astrophysical Journal
, 954
(1)
, Article 68. 10.3847/1538-4357/ace4ba.
Preview |
PDF
Brooks_Identification of Galaxy-Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning_VoR.pdf - Published Version Download (6MB) | Preview |
Abstract
We perform a search for galaxy-galaxy strong lens systems using a convolutional neural network (CNN) applied to imaging data from the first public data release of the DECam Local Volume Exploration Survey, which contains 1/4520 million astronomical sources covering 1/44000 deg2 of the southern sky to a 5σ point-source depth of g = 24.3, r = 23.9, i = 23.3, and z = 22.8 mag. Following the methodology of similar searches using Dark Energy Camera data, we apply color and magnitude cuts to select a catalog of 1/411 million extended astronomical sources. After scoring with our CNN, the highest-scoring 50,000 images were visually inspected and assigned a score on a scale from 0 (not a lens) to 3 (very probable lens). We present a list of 581 strong lens candidates, 562 of which are previously unreported. We categorize our candidates using their human-assigned scores, resulting in 55 Grade A candidates, 149 Grade B candidates, and 377 Grade C candidates. We additionally highlight eight potential quadruply lensed quasars from this sample. Due to the location of our search footprint in the northern Galactic cap (b > 10 deg) and southern celestial hemisphere (decl. < 0 deg), our candidate list has little overlap with other existing ground-based searches. Where our search footprint does overlap with other searches, we find a significant number of high-quality candidates that were previously unidentified, indicating a degree of orthogonality in our methodology. We report properties of our candidates including apparent magnitude and Einstein radius estimated from the image separation.
| Type: | Article |
|---|---|
| Title: | Identification of Galaxy-Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3847/1538-4357/ace4ba |
| Publisher version: | https://doi.org/10.3847/1538-4357%2Face4ba |
| Language: | English |
| Additional information: | © 2023 IOP Publishing. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence (http://creativecommons.org/licenses/by/4.0/). |
| 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/10178547 |
Archive Staff Only
 |
View Item |
