skip to main content

Title: Atomic Inference from Weak Gravitational Lensing Data

We present a novel approach to reconstructing the projected mass distribution from the sparse and noisy weak gravitational lensing shear data. The reconstructions are regularized via the knowledge gained from numerical simulations of clusters, with trial mass distributions constructed from n NFW profile ellipsoidal components. The parameters of these ''atoms'' are distributed a priori as in the simulated clusters. Sampling the mass distributions from the atom parameter probability density function allows estimates of the properties of the mass distribution to be generated, with error bars. The appropriate number of atoms is inferred from the data itself via the Bayesian evidence, and is typically found to be small, reecting the quality of the data. Ensemble average mass maps are found to be robust to the details of the noise realization, and succeed in recovering the demonstration input mass distribution (from a realistic simulated cluster) over a wide range of scales. As an application of such a reliable mapping algorithm, we comment on the residuals of the reconstruction and the implications for predicting convergence and shear at specific points on the sky.
Authors:
;
Publication Date:
OSTI Identifier:
877491
Report Number(s):
SLAC-PUB-11563
Journal ID: ISSN 0035-8711; MNRAA4; astro-ph/0511287; TRN: US200608%%57
DOE Contract Number:
AC02-76SF00515
Resource Type:
Journal Article
Resource Relation:
Journal Name: Monthly Notices of the Royal Astronomical Society
Research Org:
Stanford Linear Accelerator Center (SLAC)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; ATOMS; CONVERGENCE; MASS DISTRIBUTION; PROBABILITY; SAMPLING; SKY; GRAVITATIONAL LENSES Astrophysics,ASTRO