The NIFTY way of Bayesian signal inference

TL;DR

NIFTY is a versatile software library that enables flexible, grid-independent Bayesian signal inference across multiple dimensions, demonstrated through applications in astronomy and imaging.

Contribution

The paper introduces NIFTY, a novel software package that simplifies the development of grid-independent Bayesian inference algorithms for various spatial dimensions.

Findings

NIFTY supports 1D, 2D, 3D, and spherical signal inference.

It enables algorithms developed in low dimensions to be applied to complex real-world problems.

The library has been successfully applied to high energy astronomy data analysis.

Abstract

We introduce NIFTY, "Numerical Information Field Theory", a software package for the development of Bayesian signal inference algorithms that operate independently from any underlying spatial grid and its resolution. A large number of Bayesian and Maximum Entropy methods for 1D signal reconstruction, 2D imaging, as well as 3D tomography, appear formally similar, but one often finds individualized implementations that are neither flexible nor easily transferable. Signal inference in the framework of NIFTY can be done in an abstract way, such that algorithms, prototyped in 1D, can be applied to real world problems in higher-dimensional settings. NIFTY as a versatile library is applicable and already has been applied in 1D, 2D, 3D and spherical settings. A recent application is the D3PO algorithm targeting the non-trivial task of denoising, deconvolving, and decomposing photon observations in high energy astronomy.