Improving Galaxy Clustering Measurements with Deep Learning: analysis of
  the DECaLS DR7 data
  Robust measurements of cosmological parameters from galaxy surveys rely on
our understanding of systematic effects that impact the observed galaxy density
field. In this paper we present, validate, and implement the idea of adopting
the systematics mitigation method of Artificial Neural Networks for modeling
the relationship between the target galaxy density field and various
observational realities including but not limited to Galactic extinction,
seeing, and stellar density. Our method by construction allows a wide class of
models and alleviates over-training by performing k-fold cross-validation and
dimensionality reduction via backward feature elimination. By permuting the
choice of the training, validation, and test sets, we construct a selection
mask for the entire footprint. We apply our method on the extended Baryon
Oscillation Spectroscopic Survey (eBOSS) Emission Line Galaxies (ELGs)
selection from the Dark Energy Camera Legacy Survey (DECaLS) Data Release 7 and
show that the spurious large-scale contamination due to imaging systematics can
be significantly reduced by up-weighting the observed galaxy density using the
selection mask from the neural network and that our method is more effective
than the conventional linear and quadratic polynomial functions. We perform
extensive analyses on simulated mock datasets with and without systematic
effects. Our analyses indicate that our methodology is more robust to
overfitting compared to the conventional methods. This method can be utilized
in the catalog generation of future spectroscopic galaxy surveys such as eBOSS
and Dark Energy Spectroscopic Instrument (DESI) to better mitigate
observational systematics.
