Interpretation of M\"ossbauer spectra in the energy and time domain with neural networks

TL;DR

This paper presents a neural network approach for rapid and accurate analysis of Mössbauer spectra to estimate hyperfine parameters, demonstrating promising results in determining electric field gradient asymmetry.

Contribution

It introduces a neural network method for analyzing Mössbauer spectra in energy and time domains, providing a novel, fast estimation technique for hyperfine parameters.

Findings

Neural networks can estimate hyperfine parameters from Mössbauer spectra.

The method effectively determines electric field gradient asymmetry.

Initial results show promising accuracy and speed.

Abstract

An artificial neural network for extracting reasonable and fast estimates of hyperfine parameters from M\"ossbauer spectra in the energy or time domain is outlined. First promising results for determining the asymmetry of the electric field gradient at the nucleus of a diamagnetic iron center as derived with different types of neural networks are reported.