FN Archimer Export Format
PT J
TI SRoll3: A neural network approach to reduce large-scale systematic effects in the Planck High-Frequency Instrument maps
BT 
AF Lopez-Radcenco, Manuel
   Delouis, Jean Marc
   Vibert, L.
AS 1:1;2:2;3:1;
FF 1:;2:;3:;
C1 Université Paris-Saclay, CNRS, Institut d’Astrophysique Spatiale, 91405 Orsay, France
   Laboratoire d’Océanographie Physique et Spatiale, CNRS, 29238 Plouzané, France
C2 UNIV PARIS SACLAY, FRANCE
   CNRS, FRANCE
UM LOPS
IN WOS Cotutelle UMR
   copubli-france
   copubli-univ-france
IF 6.24
TC 2
UR https://archimer.ifremer.fr/doc/00753/86514/91900.pdf
LA English
DT Article
DE ;cosmology: observations;methods: data analysis;surveys;techniques: image processing
AB 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 data and the removal of large-scale systematic effects within the produced sky maps. The removal of contamination sources is made 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, which are 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 High Frequency Instrument 545 GHz Far Side Lobe simulation data, considering ideal and nonideal 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 High Frequency Instrument 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 performance in contamination removal. Importantly, the methods developed in this work are to be integrated in a new version of the SRoll algorithm (SRoll3), and here we describe SRoll3 857 GHz detector maps that were released to the community. 
PY 2021
PD JUN
SO Astronomy & Astrophysics
SN 0004-6361
PU EDP Sciences
VL 651
UT 000756551100001
DI 10.1051/0004-6361/202040152
ID 86514
ER

EF