Reducing acquisition time and radiation damage: data-driven subsampling for spectro-microscopy

TL;DR

This paper introduces data-driven subsampling strategies for spectro-microscopy that significantly reduce acquisition time and radiation damage by reconstructing full data from as little as 4-6% of measurements.

Contribution

It proposes novel spectral and spatial importance-based subsampling methods for spectro-microscopy, enabling faster experiments with less radiation exposure.

Findings

Achieves accurate data reconstruction using only 4-6% of measurements.

Reduces experiment time and radiation dose significantly.

Demonstrates effectiveness across various spectro-microscopy applications.

Abstract

Spectro-microscopy is an experimental technique which can be used to observe spatial variations in chemical state and changes in chemical state over time or under experimental conditions. As a result it has broad applications across areas such as energy materials, catalysis, environmental science and biological samples. However, the technique is often limited by factors such as long acquisition times and radiation damage. We present two measurement strategies that allow for significantly shorter experiment times and total doses applied. The strategies are based on taking only a small subset of all the measurements (e.g. sparse acquisition or subsampling), and then computationally reconstructing all unobserved measurements using mathematical techniques. The methods are data-driven, using spectral and spatial importance subsampling distributions to identify important measurements. As a result, taking as little as 4-6\% of the measurements is sufficient to capture the same information as in a conventional scan.