Interpretable, calibrated neural networks for analysis and understanding of inelastic neutron scattering data

TL;DR

This paper develops interpretable and calibrated neural networks for analyzing inelastic neutron scattering data, addressing data scarcity, uncertainty quantification, and interpretability, with applications to magnetic exchange model classification.

Contribution

It introduces a neural network approach with uncertainty quantification and interpretability techniques tailored for neutron scattering data analysis.

Findings

Uncertainty estimates improve classification reliability.

Realistic instrument resolution enhances model performance.

Class activation maps identify key spectral regions.

Abstract

Deep neural networks provide flexible frameworks for learning data representations and functions relating data to other properties and are often claimed to achieve 'super-human' performance in inferring relationships between input data and desired property. In the context of inelastic neutron scattering experiments, however, as in many other scientific scenarios, a number of issues arise: (i) scarcity of labelled experimental data, (ii) lack of uncertainty quantification on results, and (iii) lack of interpretability of the deep neural networks. In this work we examine approaches to all three issues. We use simulated data to train a deep neural network to distinguish between two possible magnetic exchange models of a half-doped manganite. We apply the recently developed deterministic uncertainty quantification method to provide error estimates for the classification, demonstrating in the process how important realistic representations of instrument resolution in the training data are for reliable estimates on experimental data. Finally we use class activation maps to determine which regions of the spectra are most important for the final classification result reached by the network.