Unlocking Hidden Information in Sparse Small-Angle Neutron Scattering Measurement

TL;DR

This paper presents a Bayesian Gaussian process regression framework that enhances small-angle neutron scattering data quality from low SNR measurements, significantly reducing measurement time while maintaining accuracy.

Contribution

It introduces a one-shot, data-efficient Bayesian method that infers high-quality SANS profiles without extensive training datasets, improving speed and reliability.

Findings

Reduces measurement time by 10 to 100 times.

Maintains accuracy in low SNR conditions.

Applicable across different SANS instruments.

Abstract

Small-angle neutron scattering (SANS) is a powerful technique for probing the nanoscale structure of materials. However, the fundamental limitations of neutron flux pose significant challenges for rapid, high-fidelity data acquisition required in many experiments. To circumvent this difficulty, we introduce a Bayesian statistical framework based on Gaussian process regression (GPR) to infer high-quality SANS intensity profiles from measurements with suboptimal signal-to-noise ratios (SNR). Unlike machine learning approaches that depend on extensive training datasets, the proposed one-shot method leverages the intrinsic mathematical properties of the scattering function, smoothness and continuity, offering a generalizable solution beyond the constraints of data-intensive techniques. By examining existing SANS experimental data, we demonstrate that this approach can reduce measurement time by between one and two orders of magnitude while maintaining accuracy and adaptability across different SANS instruments. By improving both efficiency and reliability, this method extends the capabilities of SANS, enabling broader applications in time-sensitive and low-flux experimental conditions.