Year: 2012
Type: article
Abstract: We study a regression characterization for the quadratic estimator of weak lensing, developed by Hu and Okamoto [W. Hu, Astrophys. J. 557, L79 (2001).][W. Hu and T. Okamoto, Astrophys. J. 574, 566 (2002).][T. Okamoto and W. Hu, Phys. Rev. D 67, 083002 (2003).], for cosmic microwave background observations. This characterization motivates a modification of the quadratic estimator by an adaptive Wiener filter which uses the robust Bayesian techniques described in [J. Berger, Ann. Stat. 8, 716 (1980).][J. Berger, Statistical Decision Theory and Bayesian Analysis (Springer, New York, 1980).][W. Strawderman, Ann. Stat. 42, 385 (1971).]. This technique requires the user to propose a fiducial model for the spectral density of the unknown lensing potential but the resulting estimator is developed to be robust to mis-specification of this model. The role of the fiducial spectral density is to give the estimator superior statistical performance in a ``neighborhood of the fiducial model'' while controlling the statistical errors when the fiducial spectral density is drastically wrong. Our estimate also highlights some advantages provided by a Bayesian analysis of the quadratic estimator.
Authors: Ethan Anderes, Debashis Paul
Institution: University of California, Davis
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W2155140260