Accelerating linear system solvers for time-domain component separation of cosmic microwave background data

Autor: Laura Grigori, Radek Stompor, Jan Papež
Přispěvatelé: Algorithms and parallel tools for integrated numerical simulations (ALPINES), Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jacques-Louis Lions (LJLL (UMR_7598)), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Czech Academy of Sciences [Prague] (CAS), AstroParticule et Cosmologie (APC (UMR_7164)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Centre Pierre Binétruy (CPB), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-University of California [Berkeley], University of California-University of California, The first two authors’ work was supported by the NLAFET project as part of European Union’s Horizon 2020 research and innovation program under grant 671633. This work was also supported in part by the French National Research Agency (ANR) contract ANR-17-C23-0002-01 (project B3DCMB), ANR-17-CE23-0002,B3DCMB,Big Bang à partir de Big Data (du fond diffus cosmologique)(2017), Université Paris Diderot - Paris 7 (UPD7)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Observatoire de Paris, PSL Research University (PSL)-PSL Research University (PSL)-Université Paris Diderot - Paris 7 (UPD7), The first two authors’ work was supported by the NLAFET project as part of European Union’s Horizon 2020 research and innovation program under grant 671633. This work was also supported in part by the French National Research Agency (ANR) contract ANR-17-C23-0002-01 (project B3DCMB)., PSL Research University (PSL)-PSL Research University (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Laboratoire Jacques-Louis Lions (LJLL), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Observatoire de Paris, PSL Research University (PSL)-PSL Research University (PSL)-Université Paris Diderot - Paris 7 (UPD7)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris, PSL Research University (PSL)-PSL Research University (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Cosmology and Nongalactic Astrophysics (astro-ph.CO)
Computer science
[SDU.ASTR.CO]Sciences of the Universe [physics]/Astrophysics [astro-ph]/Cosmology and Extra-Galactic Astrophysics [astro-ph.CO]
Pipeline (computing)
Posterior probability
[PHYS.MPHY]Physics [physics]/Mathematical Physics [math-ph]
FOS: Physical sciences
010103 numerical & computational mathematics
01 natural sciences
[PHYS.ASTR.CO]Physics [physics]/Astrophysics [astro-ph]/Cosmology and Extra-Galactic Astrophysics [astro-ph.CO]
[PHYS.PHYS.PHYS-COMP-PH]Physics [physics]/Physics [physics]/Computational Physics [physics.comp-ph]
Matrix (mathematics)
0103 physical sciences
FOS: Mathematics
[INFO]Computer Science [cs]
Time domain
Mathematics - Numerical Analysis
0101 mathematics
010303 astronomy & astrophysics
Linear systems solvers
Linear system
Sampling (statistics)
Astronomy and Astrophysics
Numerical Analysis (math.NA)
Maximization
Computational Physics (physics.comp-ph)
Cosmic microwave background data analysis
Data set
Component separation
Space and Planetary Science
Numerical methods
[PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph]
Algorithm
Physics - Computational Physics
Subspace topology
[MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA]
Astrophysics - Cosmology and Nongalactic Astrophysics
Zdroj: Astronomy and Astrophysics-A&A
Astronomy and Astrophysics-A&A, EDP Sciences, 2020, 638, pp.A73. ⟨10.1051/0004-6361/202037687⟩
Astronomy and Astrophysics-A&A, 2020, 638, pp.A73. ⟨10.1051/0004-6361/202037687⟩
ISSN: 0004-6361
DOI: 10.1051/0004-6361/202037687⟩
Popis: International audience; Component separation is one of the key stages of any modern cosmic microwave background data analysis pipeline. It is an inherently nonlinear procedure and typically involves a series of sequential solutions of linear systems with similar but not identical system matrices, derived for different data models of the same data set. Sequences of this type arise, for instance, in the maximization of the data likelihood with respect to foreground parameters or sampling of their posterior distribution. However, they are also common in many other contexts. In this work we consider solving the component separation problem directly in the measurement (time-) domain. This can have a number of important benefits over the more standard pixel-based methods, in particular if non-negligible time-domain noise correlations are present, as is commonly the case. The approach based on the time-domain, however, implies significant computational effort because the full volume of the time-domain data set needs to be manipulated. To address this challenge, we propose and study efficient solvers adapted to solving time-domain-based component separation systems and their sequences, and which are capable of capitalizing on information derived from the previous solutions. This is achieved either by adapting the initial guess of the subsequent system or through a so-called subspace recycling, which allows constructing progressively more efficient two-level preconditioners. We report an overall speed-up over solving the systems independently of a factor of nearly 7, or 5, in our numerical experiments, which are inspired by the likelihood maximization and likelihood sampling procedures, respectively.
Databáze: OpenAIRE
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