Astrophysical Data Analysis with Information Field Theory

被引：6
|
作者：
Ensslin, Torsten [1 ]
机构：
[1] Max Planck Inst Astrophys, D-85748 Garching, Germany
来源：
BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING, MAXENT 2013 | 2014年 / 1636卷
关键词：
LARGE-SCALE STRUCTURE; INFERENCE;
D O I：
10.1063/1.4903709
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
Non-parametric imaging and data analysis in astrophysics and cosmology can be addressed by information field theory (IFT), a means of Bayesian, data based inference on spatially distributed signal fields. IFT is a statistical field theory, which permits the construction of optimal signal recovery algorithms. It exploits spatial correlations of the signal fields even for nonlinear and non-Gaussian signal inference problems. The alleviation of a perception threshold for recovering signals of unknown correlation structure by using IFT will be discussed in particular as well as a novel improvement on instrumental self-calibration schemes. IFT can be applied to many areas. Here, applications in in cosmology (cosmic microwave background, large-scale structure) and astrophysics (galactic magnetism, radio interferometry) are presented.
引用
收藏
页码：49 / 54
页数：6
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