1 research outputs found
The PAU Survey & Euclid: Improving broad-band photometric redshifts with multi-task learning
Current and future imaging surveys require photometric redshifts (photo-z) to
be estimated for millions of galaxies. Improving the photo-z quality is a major
challenge to advance our understanding of cosmology. In this paper, we explore
how the synergies between narrow-band photometric data and large imaging
surveys can be exploited to improve broad-band photometric redshifts. We use a
multi-task learning (MTL) network to improve broad-band photo-z estimates by
simultaneously predicting the broad-band photo-z and the narrow-band photometry
from the broad-band photometry. The narrow-band photometry is only required in
the training field, which enables better photo-z predictions also for the
galaxies without narrow-band photometry in the wide field. This technique is
tested with data from the Physics of the Accelerating Universe Survey (PAUS) in
the COSMOS field. We find that the method predicts photo-z that are 14% more
precise down to magnitude i_AB<23, while reducing the outlier rate by 40% with
respect to the baseline network mapping broad-band colours to only photo-zs.
Furthermore, MTL significantly reduces the photo-z bias for high-redshift
galaxies, improving the redshift distributions for tomographic bins with z>1.
Applying this technique to deeper samples is crucial for future surveys like
\Euclid or LSST. For simulated data, training on a sample with i_AB <23, the
method reduces the photo-z scatter by 15% for all galaxies with 24<i_AB<25. We
also study the effects of extending the training sample with photometric
galaxies using PAUS high-precision photo-zs, which further reduces the photo-z
scatter.Comment: 20 pages, 16 figure