SRoll3: A neural network approach to reduce large-scale systematic effects in the Planck High Frequency Instrument maps

by   Manuel López-Radcenco, et al.

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.



There are no comments yet.


page 4

page 7

page 11

page 15


Hierarchical Learning to Solve Partial Differential Equations Using Physics-Informed Neural Networks

The Neural network-based approach to solving partial differential equati...

Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning

We present a fast simulation application based on a Deep Neural Network,...

PINNup: Robust neural network wavefield solutions using frequency upscaling and neuron splitting

Solving for the frequency-domain scattered wavefield via physics-informe...

Transfer Learning for Estimating Causal Effects using Neural Networks

We develop new algorithms for estimating heterogeneous treatment effects...

Removal of Batch Effects using Distribution-Matching Residual Networks

Sources of variability in experimentally derived data include measuremen...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.