Finding quadruply imaged quasars with machine learning-I. Methods

Akhazhanov, A. and More, A. and Desai, Shantanu and et al, . (2022) Finding quadruply imaged quasars with machine learning-I. Methods. Monthly Notices of the Royal Astronomical Society, 513 (2). pp. 2407-2421. ISSN 0035-8711

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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. © 2022 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society.

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IITH Creators:
IITH CreatorsORCiD
Desai, Shantanuhttp://orcid.org/0000-0002-0466-3288
Item Type: Article
Uncontrolled Keywords: astronomical data bases: Surveys, gravitational lensing: Strong, methods: Statistical
Subjects: Physics
Physics > Astronomy Astrophysics
Divisions: Department of Computer Science & Engineering
Department of Electrical Engineering
Department of Physics
Depositing User: . LibTrainee 2021
Date Deposited: 23 Jun 2022 11:02
Last Modified: 30 Jun 2022 07:36
URI: http://raiith.iith.ac.in/id/eprint/9368
Publisher URL: https://doi.org/10.1093/mnras/stac925
OA policy: https://v2.sherpa.ac.uk/id/publication/24618
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