Identification of GalaxyGalaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning Academic Article uri icon

abstract

  • Abstract We perform a search for galaxygalaxy 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 5 pointsource 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.

published proceedings

  • The Astrophysical Journal

author list (cited authors)

  • Zaborowski, E. A., Drlica-Wagner, A., Ashmead, F., Wu, J. F., Morgan, R., Bom, C. R., ... Weaverdyck, N.

complete list of authors

  • Zaborowski, EA||Drlica-Wagner, A||Ashmead, F||Wu, JF||Morgan, R||Bom, CR||Shajib, AJ||Birrer, S||Cerny, W||Buckley-Geer, EJ||Mutlu-Pakdil, B||Ferguson, PS||Glazebrook, K||Lozano, SJ Gonzalez||Gordon, Y||Martinez, M||Manwadkar, V||O’Donnell, J||Poh, J||Riley, A||Sakowska, JD||Santana-Silva, L||Santiago, BX||Sluse, D||Tan, CY||Tollerud, EJ||Verma, A||Carballo-Bello, JA||Choi, Y||James, DJ||Kuropatkin, N||Martínez-Vázquez, CE||Nidever, DL||Castellon, JL Nilo||Noël, NED||Olsen, KAG||Pace, AB||Mau, S||Yanny, B||Zenteno, A||Abbott, TMC||Aguena, M||Alves, O||Andrade-Oliveira, F||Bocquet, S||Brooks, D||Burke, DL||Rosell, A Carnero||Kind, M Carrasco||Carretero, J||Castander, FJ||Conselice, CJ||Costanzi, M||Pereira, MES||De Vicente, J||Desai, S||Dietrich, JP||Doel, P||Everett, S||Ferrero, I||Flaugher, B||Friedel, D||Frieman, J||García-Bellido, J||Gruen, D||Gruendl, RA||Gutierrez, G||Hinton, SR||Hollowood, DL||Honscheid, K||Kuehn, K||Lin, H||Marshall, JL||Melchior, P||Mena-Fernández, J||Menanteau, F||Miquel, R||Palmese, A||Paz-Chinchón, F||Pieres, A||Malagón, AA Plazas||Prat, J||Rodriguez-Monroy, M||Romer, AK||Sanchez, E||Scarpine, V||Sevilla-Noarbe, I||Smith, M||Suchyta, E||To, C||Weaverdyck, N

publication date

  • September 2023