The University of Southampton
University of Southampton Institutional Repository

Sparse Bayesian mass mapping with uncertainties: local credible intervals

Sparse Bayesian mass mapping with uncertainties: local credible intervals
Sparse Bayesian mass mapping with uncertainties: local credible intervals
Until recently, mass-mapping techniques for weak gravitational lensing convergence reconstruction have lacked a principled statistical framework upon which to quantify reconstruction uncertainties, without making strong assumptions of Gaussianity. In previous work, we presented a sparse hierarchical Bayesian formalism for convergence reconstruction that addresses this shortcoming. Here, we draw on the concept of local credible intervals (cf. Bayesian error bars) as an extension of the uncertainty quantification techniques previously detailed. These uncertainty quantification techniques are benchmarked against those recovered via Px-MALA – a state-of-the-art proximal Markov chain Monte Carlo (MCMC) algorithm. We find that, typically, our recovered uncertainties are everywhere conservative (never underestimate the uncertainty, yet the approximation error is bounded above), of similar magnitude and highly correlated with those recovered via Px-MALA. Moreover, we demonstrate an increase in computational efficiency of O(106) when using our sparse Bayesian approach over MCMC techniques. This computational saving is critical for the application of Bayesian uncertainty quantification to large-scale stage IV surveys such as LSST and Euclid.
Gravitational lensing: weak, Methods: data analysis, Methods: statistical, Techniques: image processing
0035-8711
394-404
Price, Matthew A.
4b9aaa38-54ba-436f-88da-bcf25c6375ea
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
McEwen, Jason D.
64c6269a-fe40-41d7-8b0c-d3c9ad920175
Pereyra, Marcelo
7ae249d9-94ea-4f67-a3ec-e2907665952e
Kitching, Thomas D.
ee37aa25-546f-4d80-bb83-495a4525da6d
Price, Matthew A.
4b9aaa38-54ba-436f-88da-bcf25c6375ea
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
McEwen, Jason D.
64c6269a-fe40-41d7-8b0c-d3c9ad920175
Pereyra, Marcelo
7ae249d9-94ea-4f67-a3ec-e2907665952e
Kitching, Thomas D.
ee37aa25-546f-4d80-bb83-495a4525da6d

Price, Matthew A., Cai, Xiaohao, McEwen, Jason D., Pereyra, Marcelo and Kitching, Thomas D. (2020) Sparse Bayesian mass mapping with uncertainties: local credible intervals. Monthly Notices of the Royal Astronomical Society, 492 (1), 394-404. (doi:10.1093/mnras/stz3453).

Record type: Article

Abstract

Until recently, mass-mapping techniques for weak gravitational lensing convergence reconstruction have lacked a principled statistical framework upon which to quantify reconstruction uncertainties, without making strong assumptions of Gaussianity. In previous work, we presented a sparse hierarchical Bayesian formalism for convergence reconstruction that addresses this shortcoming. Here, we draw on the concept of local credible intervals (cf. Bayesian error bars) as an extension of the uncertainty quantification techniques previously detailed. These uncertainty quantification techniques are benchmarked against those recovered via Px-MALA – a state-of-the-art proximal Markov chain Monte Carlo (MCMC) algorithm. We find that, typically, our recovered uncertainties are everywhere conservative (never underestimate the uncertainty, yet the approximation error is bounded above), of similar magnitude and highly correlated with those recovered via Px-MALA. Moreover, we demonstrate an increase in computational efficiency of O(106) when using our sparse Bayesian approach over MCMC techniques. This computational saving is critical for the application of Bayesian uncertainty quantification to large-scale stage IV surveys such as LSST and Euclid.

Full text not available from this repository.

More information

Accepted/In Press date: 4 December 2019
e-pub ahead of print date: 10 December 2019
Published date: February 2020
Keywords: Gravitational lensing: weak, Methods: data analysis, Methods: statistical, Techniques: image processing

Identifiers

Local EPrints ID: 438782
URI: http://eprints.soton.ac.uk/id/eprint/438782
ISSN: 0035-8711
PURE UUID: 04ef08d7-282e-42fc-8740-a3a7348c5920

Catalogue record

Date deposited: 24 Mar 2020 17:30
Last modified: 14 Sep 2021 17:43

Export record

Altmetrics

Contributors

Author: Matthew A. Price
Author: Xiaohao Cai
Author: Jason D. McEwen
Author: Marcelo Pereyra
Author: Thomas D. Kitching

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×