Finding quadruply imaged quasars with machine learning. I. Methods
Creators: name: Akhazhanov, A.
name: More, A.
name: Amini, A.
name: Hazlett, C.
name: Treu, T.
name: Birrer, S.
name: Shajib, A.
name: Schechter, P.
name: Lemon, C.
name: Nord, B.
name: Aguena, M.
name: Allam, S.
name: Andrade-Oliveira, F.
name: Annis, J.
name: Brooks, D.
name: Buckley-Geer, E.
name: Burke, D. L.
name: Rosell, A. Carnero
name: Kind, M. Carrasco
name: Carretero, J.
name: Choi, A.
name: Conselice, C.
name: Costanzi, M.
name: da Costa, L. N.
name: Pereira, M. E. S.
name: De Vicente, J.
name: Desai, S.
name: Dietrich, J. P.
name: Doel, P.
name: Everett, S.
name: Ferrero, I.
name: Finley, D. A.
name: Flaugher, B.
name: Frieman, J.
name: García-Bellido, J.
name: Gerdes, D. W.
name: Gruen, D.
name: Gruendl, R. A.
name: Gschwend, J.
name: Gutierrez, G.
name: Hinton, S. R.
name: Hollowood, D. L.
name: Honscheid, K.
name: James, D. J.
name: Kim, A. G.
name: Kuehn, K.
name: Kuropatkin, N.
name: Lahav, O.
name: Lima, M.
name: Lin, H.
name: Maia, M. A. G.
name: March, M.
name: Menanteau, F.
name: Miquel, R.
name: Morgan, R.
name: Palmese, A.
name: Paz-Chinchón, F.
name: Pieres, A.
name: Malagón, A. A. Plazas
name: Sanchez, E.
name: Scarpine, V.
name: Serrano, S.
name: Sevilla-Noarbe, I.
name: Smith, M.
name: Soares-Santos, M.
name: Suchyta, E.
name: Swanson, M. E. C.
name: Tarle, G.
name: To, C.
name: Varga, T. N.
name: Weller, J.
Publisher: name: arXiv
Year: 2021
Subjects: subject: Cosmology and Nongalactic Astrophysics (astro-ph.CO)
subject: FOS: Physical sciences
subject: FOS: Physical sciences

description: Strongly lensed quadruply imaged quasars (quads) are extraordinary objects. They are very rare in the sky -- only a few tens are known to date -- and yet they provide unique information about a wide range of topics, including the expansion history and the composition of the Universe, the distribution of stars and dark matter in galaxies, the host galaxies of quasars, and the stellar initial mass function. Finding them in astronomical images is a classic "needle in a haystack" problem, as they are outnumbered by other (contaminant) sources by many orders of magnitude. To solve this problem, we develop state-of-the-art deep learning methods and train them on realistic simulated quads based on real images of galaxies taken from the Dark Energy Survey, with realistic source and deflector models, including the chromatic effects of microlensing. The performance of the best methods on a mixture of simulated and real objects is excellent, yielding area under the receiver operating curve in the range 0.86 to 0.89. Recall is close to 100% down to total magnitude i~21 indicating high completeness, while precision declines from 85% to 70% in the range i~17-21. The methods are extremely fast: training on 2 million samples takes 20 hours on a GPU machine, and 10^8 multi-band cutouts can be evaluated per GPU-hour. The speed and performance of the method pave the way to apply it to large samples of astronomical sources, bypassing the need for photometric pre-selection that is likely to be a major cause of incompleteness in current samples of known quads.
description: 17 pages, 14 figures, submitted to MNRAS