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AMBER: Algorithm for Multiplexing spectrometer Background Estimation with Rotation-independence
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DOI：10.1016/j.softx.2025.102340.png)
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Abstract
En 中文
State-of-the art neutron spectrometers enable simultaneous measurements of high-dimensional datasets, allowing fora large collection rate of dynamic material properties. In this paper, we present the Algorithm for Multiplexing spectrometer Background Estimation with Rotation-independence (AMBER), which is a segmentation algorithm designed to decompose measured neutron scattering data into model-agnostic foreground and background contributions. The method takes advantage of the fact that background and foreground signals are measured simultaneously during the data collection process, relying on rotational independence of background contributions. The algorithm, initially developed for multiplexing neutron spectrometers , aims to strongly reduce time consuming expert input, therefore promoting full data set usage while minimizing the source of systematic errors.
Keywords:
Inelastic neutron scattering
Machine learning
Signal decomposition
Background determination
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