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

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dc.contributor.authorE. A. Zaborowski
dc.contributor.authorA. Drlica-Wagner
dc.contributor.authorF. Ashmead
dc.contributor.authorJ. F. Wu
dc.date.accessioned2024-05-22T23:20:01Z
dc.date.available2024-05-22T23:20:01Z
dc.date.issued2023-08-23
dc.description.abstractWe 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.
dc.identifier.citationZaborowski, E. A., Drlica-Wagner, A., Ashmead, F., Wu, J. F., Morgan, R., Bom, C. R., ... & Weaverdyck, N. (2023). Identification of Galaxy–Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning. The Astrophysical Journal, 954(1), 68.
dc.identifier.urihttps://conocimientoabierto.online/handle/123456789/22
dc.language.isoen
dc.publisherIOP
dc.titleIdentification of Galaxy–Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning
dc.typeArticle

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