Hierarchical Bayesian approach for adaptive integration of Bragg peaks in time-of-flight neutron scattering data
Viktor Reshniak, Xiaoping Wang, Guannan Zhang, Siyan Liu, Junqi Yin

TL;DR
This paper introduces a Bayesian method for adaptive detection of Bragg peaks in time-of-flight neutron scattering data, improving analysis accuracy by handling varying sampling rates and avoiding information loss in weak reflections.
Contribution
The paper presents a novel Bayesian approach that adaptively identifies Bragg peaks in TOF neutron data, outperforming traditional histogram fitting methods.
Findings
Effective detection of Bragg peaks in real experimental data
Improved signal-to-noise ratio for weak reflections
No information loss due to adaptive Bayesian modeling
Abstract
The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) operates in the event mode. Time-of-flight (TOF) information about each detected neutron is collected separately and saved as a descriptive entry in a database enabling unprecedented accuracy of the collected experimental data. Nevertheless, the common data processing pipeline still involves the binning of data to perform analysis and feature extraction. For weak reflections, improper binning leads to sparse histograms with low signal-to-noise ratios, rendering them uninformative. In this study, we propose the Bayesian approach for the identification of Bragg peaks in TOF diffraction data. The method is capable of adaptively handling the varying sampling rates found in different regions of the reciprocal space. Unlike histogram fitting methods, our approach focuses on estimating the true neutron flux function.…
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Taxonomy
TopicsNuclear Physics and Applications · Nuclear reactor physics and engineering · Seismic Imaging and Inversion Techniques
