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

Journal article

Artículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar, el nombre del grupo de colaboración, si le hubiere, y los autores pertenecientes a la UAMWe 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

Authors: Zaborowski, EA; Drlica-Wagner, A; Ashmead, F; Wu, JF; Morgan, R

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: American Astronomical Society
• Journal: The Astrophysical Journal
• Volume: 954
• Issue: 1
• Pages: 68-68
• Publication date: 2023-08-23
• DOI: 10.3847/1538-4357/ace4ba
• EISSN: 1538-4357
• ISSN: 0004-637X
• Language: English
• Keywords: Artificial intelligence; Physics; Milky Way; Convolutional neural network; Sky; Galaxy; Computer science; Lens (geology); Optics; Astrophysics; Astronomy