In:
The Astrophysical Journal, American Astronomical Society, Vol. 954, No. 1 ( 2023-09-01), p. 68-
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 ∼520 million astronomical sources covering ∼4000 deg 2 of the southern sky to a 5 σ 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 ∼11 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 of Medium:
Online Resource
ISSN:
0004-637X
,
1538-4357
DOI:
10.3847/1538-4357/ace4ba
Language:
Unknown
Publisher:
American Astronomical Society
Publication Date:
2023
detail.hit.zdb_id:
2207648-7
detail.hit.zdb_id:
1473835-1
SSG:
16,12
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