# Noisy independent component analysis of auto-correlated components

## TL;DR

This paper introduces a novel method for separating auto-correlated, noisy, independent components from multi-channel data, enhancing signal recovery even in high noise conditions by leveraging information field theory.

## Contribution

The method uniquely combines component separation, noise handling, and instrument characteristics within an information field theory framework, applicable across various dimensions.

## Key findings

- Effective separation in high noise regimes
- Inclusion of instrument characteristics improves accuracy
- Provides error estimates via independent posterior samples

## Abstract

We present a new method for the separation of superimposed, independent, auto-correlated components from noisy multi-channel measurement. The presented method simultaneously reconstructs and separates the components, taking all channels into account and thereby increases the effective signal-to-noise ratio considerably, allowing separations even in the high noise regime. Characteristics of the measurement instruments can be included, allowing for application in complex measurement situations. Independent posterior samples can be provided, permitting error estimates on all desired quantities. Using the concept of information field theory, the algorithm is not restricted to any dimensionality of the underlying space or discretization scheme thereof.