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.
- Research Organization:
- SLAC National Accelerator Lab., Menlo Park, CA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC02-76SF00515
- OSTI ID:
- 877491
- Report Number(s):
- SLAC-PUB-11563; MNRAA4; astro-ph/0511287; TRN: US200608%%57
- Journal Information:
- Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society; ISSN 0035-8711
- Country of Publication:
- United States
- Language:
- English
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