Marginal unbiased score expansion and application to CMB lensing
We present the marginal unbiased score expansion (MUSE) method, an algorithm for generic high-dimensional hierarchical Bayesian inference. MUSE performs approximate marginalization over arbitrary non-Gaussian latent parameter spaces, yielding Gaussianized asymptotically unbiased and near-optimal con...
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ftcdlib:oai:escholarship.org:ark:/13030/qt94m9f1pc 2024-01-14T10:10:46+01:00 Marginal unbiased score expansion and application to CMB lensing Millea, Marius Seljak, Uroš 103531 2022-05-15 application/pdf https://escholarship.org/uc/item/94m9f1pc unknown eScholarship, University of California qt94m9f1pc https://escholarship.org/uc/item/94m9f1pc public Physical Review D, vol 105, iss 10 Mathematical Sciences Statistics Astronomical and Space Sciences Atomic Molecular Nuclear Particle and Plasma Physics Quantum Physics Nuclear & Particles Physics Mathematical physics Astronomical sciences Particle and high energy physics article 2022 ftcdlib 2023-12-18T19:06:32Z We present the marginal unbiased score expansion (MUSE) method, an algorithm for generic high-dimensional hierarchical Bayesian inference. MUSE performs approximate marginalization over arbitrary non-Gaussian latent parameter spaces, yielding Gaussianized asymptotically unbiased and near-optimal constraints on global parameters of interest. It is computationally much cheaper than exact alternatives like Hamiltonian Monte Carlo (HMC), excelling on funnel problems which challenge HMC, and does not require any problem-specific user supervision like other approximate methods such as variational inference or many simulation-based inference methods. MUSE makes possible the first joint Bayesian estimation of the delensed Cosmic Microwave Background (CMB) power spectrum and gravitational lensing potential power spectrum, demonstrated here on a simulated data set as large as the upcoming South Pole Telescope 3G 1500 deg2 survey, corresponding to a latent dimensionality of ∼6 million and of order 100 global bandpower parameters. On a subset of the problem where an exact but more expensive HMC solution is feasible, we verify that MUSE yields nearly optimal results. We also demonstrate that existing spectrum-based forecasting tools which ignore pixel-masking underestimate predicted error bars by only ∼10%. This method is a promising path forward for fast lensing and delensing analyses which will be necessary for future CMB experiments such as SPT-3G, Simons Observatory, or CMB-S4, and can complement or supersede existing HMC approaches. The success of MUSE on this challenging problem strengthens its case as a generic procedure for a broad class of high-dimensional inference problems. Article in Journal/Newspaper South pole University of California: eScholarship South Pole |
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University of California: eScholarship |
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Mathematical Sciences Statistics Astronomical and Space Sciences Atomic Molecular Nuclear Particle and Plasma Physics Quantum Physics Nuclear & Particles Physics Mathematical physics Astronomical sciences Particle and high energy physics |
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Mathematical Sciences Statistics Astronomical and Space Sciences Atomic Molecular Nuclear Particle and Plasma Physics Quantum Physics Nuclear & Particles Physics Mathematical physics Astronomical sciences Particle and high energy physics Millea, Marius Seljak, Uroš Marginal unbiased score expansion and application to CMB lensing |
topic_facet |
Mathematical Sciences Statistics Astronomical and Space Sciences Atomic Molecular Nuclear Particle and Plasma Physics Quantum Physics Nuclear & Particles Physics Mathematical physics Astronomical sciences Particle and high energy physics |
description |
We present the marginal unbiased score expansion (MUSE) method, an algorithm for generic high-dimensional hierarchical Bayesian inference. MUSE performs approximate marginalization over arbitrary non-Gaussian latent parameter spaces, yielding Gaussianized asymptotically unbiased and near-optimal constraints on global parameters of interest. It is computationally much cheaper than exact alternatives like Hamiltonian Monte Carlo (HMC), excelling on funnel problems which challenge HMC, and does not require any problem-specific user supervision like other approximate methods such as variational inference or many simulation-based inference methods. MUSE makes possible the first joint Bayesian estimation of the delensed Cosmic Microwave Background (CMB) power spectrum and gravitational lensing potential power spectrum, demonstrated here on a simulated data set as large as the upcoming South Pole Telescope 3G 1500 deg2 survey, corresponding to a latent dimensionality of ∼6 million and of order 100 global bandpower parameters. On a subset of the problem where an exact but more expensive HMC solution is feasible, we verify that MUSE yields nearly optimal results. We also demonstrate that existing spectrum-based forecasting tools which ignore pixel-masking underestimate predicted error bars by only ∼10%. This method is a promising path forward for fast lensing and delensing analyses which will be necessary for future CMB experiments such as SPT-3G, Simons Observatory, or CMB-S4, and can complement or supersede existing HMC approaches. The success of MUSE on this challenging problem strengthens its case as a generic procedure for a broad class of high-dimensional inference problems. |
format |
Article in Journal/Newspaper |
author |
Millea, Marius Seljak, Uroš |
author_facet |
Millea, Marius Seljak, Uroš |
author_sort |
Millea, Marius |
title |
Marginal unbiased score expansion and application to CMB lensing |
title_short |
Marginal unbiased score expansion and application to CMB lensing |
title_full |
Marginal unbiased score expansion and application to CMB lensing |
title_fullStr |
Marginal unbiased score expansion and application to CMB lensing |
title_full_unstemmed |
Marginal unbiased score expansion and application to CMB lensing |
title_sort |
marginal unbiased score expansion and application to cmb lensing |
publisher |
eScholarship, University of California |
publishDate |
2022 |
url |
https://escholarship.org/uc/item/94m9f1pc |
op_coverage |
103531 |
geographic |
South Pole |
geographic_facet |
South Pole |
genre |
South pole |
genre_facet |
South pole |
op_source |
Physical Review D, vol 105, iss 10 |
op_relation |
qt94m9f1pc https://escholarship.org/uc/item/94m9f1pc |
op_rights |
public |
_version_ |
1788065575417675776 |