  In the present work, we propose a neural network based data inversion
approach to reduce structured contamination sources, with a particular focus on
the mapmaking for Planck High Frequency Instrument (Planck-HFI) data and the
removal of large-scale systematic effects within the produced sky maps. The
removal of contamination sources is rendered possible by the structured nature
of these sources, which is characterized by local spatiotemporal interactions
producing couplings between different spatiotemporal scales. We focus on
exploring neural networks as a means of exploiting these couplings to learn
optimal low-dimensional representations, optimized with respect to the
contamination source removal and mapmaking objectives, to achieve robust and
effective data inversion. We develop multiple variants of the proposed
approach, and consider the inclusion of physics informed constraints and
transfer learning techniques. Additionally, we focus on exploiting data
augmentation techniques to integrate expert knowledge into an otherwise
unsupervised network training approach. We validate the proposed method on
Planck-HFI 545 GHz Far Side Lobe simulation data, considering ideal and
non-ideal cases involving partial, gap-filled and inconsistent datasets, and
demonstrate the potential of the neural network based dimensionality reduction
to accurately model and remove large-scale systematic effects. We also present
an application to real Planck-HFI 857 GHz data, which illustrates the relevance
of the proposed method to accurately model and capture structured contamination
sources, with reported gains of up to one order of magnitude in terms of
contamination removal performance. Importantly, the methods developed in this
work are to be integrated in a new version of the SRoll algorithm (SRoll3), and
we describe here SRoll3 857 GHz detector maps that will be released to the
community.
