http://arxiv.org/abs/2210.10802
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 select a catalog of $\sim 11$ million extended astronomical sources with $16 < g < 22$, $17.2 < r < 22$, $15 < i < 21$, $0 < g – i < 3$, and $-0.2 < g – r < 1.75$. 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 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 (${\rm Dec.}<0$ 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 which were previously unidentified, indicating a degree of orthogonality in our methodology. We categorize our candidates using their human-assigned scores and report properties including apparent magnitude and Einstein radius estimated from the image separation.
E. Zaborowski, A. Drlica-Wagner, F. Ashmead, et. al.
Fri, 21 Oct 22
28/76
Comments: N/A