Identification of Galaxy-Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning

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 (DELVE), which contains \(\sim 520\) million astronomical sources covering \(\sim 4,000\) \(\mathrm{deg}^2\) of the southern sky to a \(5\sigma\) 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 DECam data, we apply color and magnitude cuts to select a catalog of \(\sim 11\) 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 (definitely not a lens) to 3 (very probable lens). We present a list of 617 strong lens candidates, 599 of which are previously unreported. We categorize our candidates using their human-assigned scores, resulting in 60 Grade A candidates, 160 Grade B candidates, and 397 Grade C candidates. We additionally highlight 8 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 (\({\rm Dec.}