Title: Sparse Bayesian mass mapping with uncertainties: peak statistics and feature locations

Abstract

ABSTRACT Weak lensing convergence maps – upon which higher order statistics can be calculated – can be recovered from observations of the shear field by solving the lensing inverse problem. For typical surveys this inverse problem is ill-posed (often seriously) leading to substantial uncertainty on the recovered convergence maps. In this paper we propose novel methods for quantifying the Bayesian uncertainty in the location of recovered features and the uncertainty in the cumulative peak statistic – the peak count as a function of signal-to-noise ratio (SNR). We adopt the sparse hierarchical Bayesian mass-mapping framework developed in previous work, which provides robust reconstructions and principled statistical interpretation of reconstructed convergence maps without the need to assume or impose Gaussianity. We demonstrate our uncertainty quantification techniques on both Bolshoi N-body (cluster scale) and Buzzard V-1.6 (large-scale structure) N-body simulations. For the first time, this methodology allows one to recover approximate Bayesian upper and lower limits on the cumulative peak statistic at well-defined confidence levels.

Authors:

Price, M. A. ^[1]; McEwen, J. D. ^[1]; Cai, X. ^[1]; Kitching  ^[2]

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+ Show Author Affiliations

1. Mullard Space Science Laboratory, University College London, London RH5 6NT, UK
2. (for the LSST Dark Energy Science Collaboration), T. D. [Mullard Space Science Laboratory, University College London, London RH5 6NT, UK

Publication Date:

Mon Aug 26 00:00:00 EDT 2019

Research Org.:

Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Stanford Univ., CA (United States)

Sponsoring Org.:

USDOE Office of Science (SC); Engineering and Physical Sciences Research Council (EPSRC)

OSTI Identifier:

1561394

Alternate Identifier(s):

OSTI ID: 1577801

Grant/Contract Number:  

AC02-05CH11231; AC02-76SF00515; EP/M0110891

Resource Type:

Published Article

Journal Name:

Monthly Notices of the Royal Astronomical Society

Additional Journal Information:

Journal Name: Monthly Notices of the Royal Astronomical Society Journal Volume: 489 Journal Issue: 3; Journal ID: ISSN 0035-8711

Publisher:

Royal Astronomical Society

Country of Publication:

United Kingdom

Language:

English

Subject:

79 ASTRONOMY AND ASTROPHYSICS; astronomy & astrophysics; gravitational lensing: weak; methods: statistical; techniques: image processing; cosmological parameters; dark matter