Multi-resolution Bayesian CMB component separation through Wiener-filtering with a pseudo-inverse preconditioner

Abstract

We present a Bayesian model for multi-resolution CMB component separation based on Wiener filtering and/or computation of constrained realizations, extending a previously developed framework. We also develop an efficient solver for the corresponding linear system for the associated signal amplitudes. The core of this new solver is an efficient preconditioner based on the pseudo-inverse of the coefficient matrix of the linear system. In the full sky coverage case, the method gives a speed-up of 2--3x in compute time compared to a simple diagonal preconditioner, and it is easier to implement in terms of practical computer code. In the case where a mask is applied and prior-driven constrained realization is sought within the mask, this is the first time full convergence has been achieved at the full resolution of the Planck dataset. Prototype benchmark code is available at https://github.com/dagss/cmbcr .

13 pages, 10 figures, Submitted to A&A

References in corpus (3)

• Planck 2018 results. IV. Diffuse component separation
• Efficient Wiener filtering without preconditioning
• Wiener filter reloaded: fast signal reconstruction without preconditioning

Cited by in corpus (16)

• Planck 2018 results. IV. Diffuse component separation
• Planck intermediate results. LVII. Joint Planck LFI and HFI data processing
• BeyondPlanck III. Commander3
• Foreground Separation and Constraints on Primordial Gravitational Waves with the PICO Space Mission
• BeyondPlanck I. Global Bayesian analysis of the Planck Low Frequency Instrument data
• Efficient Optimal Reconstruction of Linear Fields and Band-powers from Cosmological Data
• BeyondPlanck XIII. Intensity foreground sampling, degeneracies, and priors
• The Atacama Cosmology Telescope: Map-Based Noise Simulations for DR6
• Wiener filtering and pure E/B decomposition of CMB maps with anisotropic correlated noise
• BeyondPlanck XII. Cosmological parameter constraints with end-to-end error propagation
• BeyondPlanck XI. Bayesian CMB analysis with sample-based end-to-end error propagation
• Hierarchical Bayesian CMB Component Separation with the No-U-Turn Sampler
• Optimal and fast E/B separation with a dual messenger field
• Accelerating linear system solvers for time domain component separation of cosmic microwave background data
• Application of Genetic Algorithm to Estimate the Large Angular Scale Features of Cosmic Microwave Background
• Cosmoglobe: Towards end-to-end CMB cosmological parameter estimation without likelihood approximations