Utilizing hidden Markov processes as a new tool for experimental physics

TL;DR

This paper introduces a novel application of hidden Markov processes as a powerful analytical tool for experimental physics, enabling extraction of physical parameters from complex systems' data.

Contribution

It bridges experimental physics and advanced algorithms by proposing a physically oriented hidden Markov process for data analysis, a new approach in the field.

Findings

Effective analysis of electronic systems with resistance noise

Potential to become a standard technique in experimental physics

Demonstrated usefulness on low-dimensional systems

Abstract

A hidden Markov process is a well known concept in information theory and is used for a vast range of applications such as speech recognition and error correction. We bridge between two disciplines, experimental physics and advanced algorithms, and propose to use a physically oriented hidden Markov process as a new tool for analyzing experimental data. This tool enables one to extract valuable information on physical parameters of complex systems. We demonstrate the usefulness of this technique on low dimensional electronic systems which exhibit time dependent resistance noise. This method is expected to become a standard technique in experimental physics.