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

Zaborowski, E. A.; Drlica-Wagner, A.; Ashmead, F.; Wu, J. F.; Morgan, R.; Bom, C. R.; Shajib, A. J.; Birrer, S.; Cerny, W.; Buckley-Geer, E. J.; Mutlu-Pakdil, B.; Ferguson, P. S.; Glazebrook, K.; Lozano, S. Gonzalez; Gordon, Y.; Martinez, M.; Manwadkar, V.; O’Donnell, J.; Poh, J.; Riley, A.; Sakowska, J. D.; Santana-Silva, L.; Santiago, B. X.; Sluse, D.; Tan, C. Y.; Tollerud, E. J.; Verma, A.; Carballo-Bello, J. A.; Choi, Y.; James, D. J.; Kuropatkin, N.; Martínez-Vázquez, C. E.; Nidever, D. L.; Castellon, J. Nilo; Noël, N. D.; Olsen, K. G.; Pace, A. B.; Mau, S.; Yanny, B.; Zenteno, A.; Abbott, T. C.; Aguena, M.; Alves, O.; Andrade-Oliveira, F.; Bocquet, S.; Brooks, D.; Burke, D. L.; Carnero Rosell, A.; Carrasco Kind, M.; Carretero, J.; Castander, F. J.; Conselice, C. J.; Costanzi, M.; Pereira, M. S.; De Vicente, J.; Desai, S.; Dietrich, J. P.; Doel, P.; Everett, S.; Ferrero, I.; Flaugher, B.; Friedel, D.; Frieman, J.; García-Bellido, J.; Gruen, D.; Gruendl, R. A.; Gutierrez, G.; Hinton, S. R.; Hollowood, D. L.; Honscheid, K.; Kuehn, K.; Lin, H.; Marshall, J. L.; Melchior, P.; Mena-Fernández, J.; Menanteau, F.; Miquel, R.; Palmese, A.; Paz-Chinchón, F.; Pieres, A.; Malagón, A. Plazas; Prat, J.; Rodriguez-Monroy, M.; Romer, A. K.; Sanchez, E.; Scarpine, V.; Sevilla-Noarbe, I.; Smith, M.; Suchyta, E.; To, C.; Weaverdyck, N.

doi:10.3847/1538-4357/ace4ba

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

Journal Article · 23 August 2023 · The Astrophysical Journal

DOI: https://doi.org/10.3847/1538-4357/ace4ba · OSTI ID:1906066

^[1];

; Ashmead, F.;

;

; Manwadkar, V.;

; Poh, J.;

The Ohio State University, Columbus, OH (United States); University of Chicago, IL (United States). Kavli Institute for Cosmological Physics (KICP). DES Collaboration. et al.

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.

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Research Organization:: Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), High Energy Physics (HEP)

Contributing Organization:: DES Collaboration; DELVE Collaboration

Grant/Contract Number:: AC02-07CH11359; AC05-00OR22725

OSTI ID:: 1906066

Alternate ID(s):: OSTI ID: 2000343

Report Number(s):: FERMILAB-PUB-22-759-LDRD-PPD; DES-2022-0734; arXiv:2210.10802; oai:inspirehep.net:2611762

Journal Information:: The Astrophysical Journal, Vol. 954, Issue 1; ISSN 0004-637X

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United States

Language:: English

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