  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.

Cosmology and Nongalactic Astrophysics