Astrophysical data analysis with information field theory

TL;DR

This paper introduces information field theory (IFT) as a Bayesian framework for non-parametric astrophysical data analysis, enabling optimal signal recovery by exploiting spatial correlations even in complex, nonlinear, and non-Gaussian scenarios.

Contribution

It presents IFT as a novel statistical field theory approach that improves signal inference and calibration in astrophysics and cosmology applications.

Findings

IFT alleviates perception thresholds in signal recovery.

IFT enables improved instrumental self-calibration.

Applications demonstrated in cosmology and astrophysics contexts.

Abstract

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.