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

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 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 ∼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.

Detectability of Strongly Gravitationally Lensed Tidal Disruption Events

“…However, we note here that f host is mostly limited by the human inspection criteria (see Chen et al 2022 for detailed description) on the shape recognition of the lensed galaxies rather than the limiting magnitude. With the rapid development of the strong machine-learning technique on the galaxy-galaxy lensing identification (e.g., Hezaveh et al 2017; Lanusse et al 2018;Zaborowski et al 2023), one may identify the lensed hosts more accurately, making f host ∼ 1 as expected. Therefore, we are optimistic about the detection of lensed TDEs and hopefully may help to resolve several important physical aspects involved in TDEs.…”

Astronomical or political: Interpretation of comets in times of crisis in Qing China

“…However, we note here that f host is mostly limited by the humaninspection criteria (see Chen et al (2022) for detailed description ) on the shape recognition of the lensed galaxies rather than the limiting magnitude. With the rapid development of the strong machine-learning technique on the galaxy-galaxy lensing identification (e.g., Hezaveh et al 2017;Lanusse et al 2018;Zaborowski et al 2023), one may identify the lensed hosts more accurately, making f host ∼ 1 as expected. Therefore, we are optimicstic of the detection of lensed TDEs and hopefully may help to resolve several important physical aspects involved in TDEs.…”