Finding quadruply imaged quasars with machine learning-I. Methods

DES Collaboration, A. Akhazhanov, A. More, A. Amini, C. Hazlett, T. Treu*, S. Birrer, A. Shajib, K. Liao, C. Lemon, A. Agnello, B. Nord, M. Aguena, S. Allam, F. Andrade-Oliveira, J. Annis, D. Brooks, E. Buckley-Geer, D. L. Burke, A. Carnero RosellM. Carrasco Kind, J. Carretero, A. Choi, C. Conselice, M. Costanzi, L. N. da Costa, M. E. S. Pereira, J. De Vicente, S. Desai, J. P. Dietrich, P. Doel, S. Everett, I. Ferrero, D. A. Finley, B. Flaugher, J. Frieman, J. Garciá-Bellido, D. W. Gerdes, D. Gruen, R. A. Gruendl, J. Gschwend, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. J. James, A. G. Kim, K. Kuehn, N. Kuropatkin, O. Lahav, M. Lima, H. Lin, M. A. G. Maia, M. March, F. Menanteau, R. Miquel, R. Morgan, A. Palmese, F. Paz-Chinchón, A. Pieres, A. A. Plazas Malagón, E. Sanchez, V. Scarpine, S. Serrano, I. Sevilla-Noarbe, M. Smith, M. Soares-Santos, E. Suchyta, M. E. C. Swanson, G. Tarle, C. To, T. N. Varga, J. Weller

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Strongly lensed quadruply imaged quasars (quads) are extraordinary objects. They are very rare in the sky 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 of 0.86-0.89. Recall is close to 100 per cent down to total magnitude i ∼21 indicating high completeness, while precision declines from 85 per cent to 70 per cent in the range i ∼17-21. The methods are extremely fast: Training on 2 million samples takes 20 h on a GPU machine, and 108 multiband cut-outs 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.

Original languageEnglish
Pages (from-to)2407-2421
Number of pages15
JournalMonthly Notices of the Royal Astronomical Society
Volume513
Issue number2
DOIs
Publication statusPublished - Jun 2022

Keywords

  • astronomical data bases: Surveys
  • gravitational lensing: Strong
  • methods: Statistical

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