Universal Convergence Metric for Time-Resolved Neutron Scattering

TL;DR

This paper presents a universal, model-independent convergence metric for time-resolved SANS that predicts optimal measurement duration early in the experiment, enabling real-time optimization and efficiency improvements.

Contribution

The work introduces a dimensionless convergence metric based on Gaussian Process Regression that reveals a universal power-law scaling in profile evolution across soft matter systems.

Findings

Discovered a universal power-law scaling with an exponent between -2 and -1.

The metric predicts measurement sufficiency within the first ten time steps.

Supports real-time experimental optimization in low-flux neutron sources.

Abstract

This work introduces a model-independent, dimensionless metric for predicting optimal measurement duration in time-resolved Small-Angle Neutron Scattering (SANS) using early-time data. Built on a Gaussian Process Regression (GPR) framework, the method reconstructs scattering profiles with quantified uncertainty, even from sparse or noisy measurements. Demonstrated on the EQSANS instrument at the Spallation Neutron Source, the approach generalizes to general SANS instruments with a two-dimensional detector. A key result is the discovery of a dimensionless convergence metric revealing a universal power-law scaling in profile evolution across soft matter systems. When time is normalized by a system-specific characteristic time $t^{\star}$, the variation in inferred profiles collapses onto a single curve with an exponent between $-2$ and $-1$. This trend emerges within the first ten time steps, enabling early prediction of measurement sufficiency. The method supports real-time experimental optimization and is especially valuable for maximizing efficiency in low-flux environments such as compact accelerator-based neutron sources.