Photometry of high-redshift blended galaxies using deep learning
  The new generation of deep photometric surveys requires unprecedentedly
precise shape and photometry measurements of billions of galaxies to achieve
their main science goals. At such depths, one major limiting factor is the
blending of galaxies due to line-of-sight projection, with an expected fraction
of blended galaxies of up to 50%. Current deblending approaches are in most
cases either too slow or not accurate enough to reach the level of
requirements. This work explores the use of deep neural networks to estimate
the photometry of blended pairs of galaxies in monochrome space images, similar
to the ones that will be delivered by the Euclid space telescope. Using a clean
sample of isolated galaxies from the CANDELS survey, we artificially blend them
and train two different network models to recover the photometry of the two
galaxies. We show that our approach can recover the original photometry of the
galaxies before being blended with $\sim$7% accuracy without any human
intervention and without any assumption on the galaxy shape. This represents an
improvement of at least a factor of 4 compared to the classical SExtractor
approach. We also show that forcing the network to simultaneously estimate a
binary segmentation map results in a slightly improved photometry. All data
products and codes will be made public to ease the comparison with other
approaches on a common data set.
