Machine learning based unfolding of x-ray spectra from filter stack spectrometer data

Alvarez, M. Alvarado; Wolfe, B. T.; Wong, C. -S.; Broughton, D. P.; Huang, C. -K.; Reinovsky, R. E.; Wang, Z.; Batha, S. H.

doi:10.1063/5.0216759

Machine learning based unfolding of x-ray spectra from filter stack spectrometer data

Journal Article · Thu Aug 01 00:00:00 EDT 2024 · Review of Scientific Instruments

DOI:https://doi.org/10.1063/5.0216759· OSTI ID:2480461

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Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

We demonstrate the application of neural networks to perform x-ray spectra unfolding from data collected by filter stack spectrometers. A filter stack spectrometer consists of a series of filter-detector pairs, where the detectors behind each filter measure the energy deposition through each layer as photo-stimulated luminescence (PSL). The network is trained on synthetic data, assuming x-rays of energies < 1 MeV and of two different distribution functions (Maxwellian and Gaussian) and the corresponding measured PSL values obtained from five different filter stack spectrometer designs. Predicted unfolds of single distributions are near identical reproductions of the ground truth spectra, with differences in the values lower than 20% at the higher energy end in some cases. The neural network has also demonstrated robustness to experimental measurement errors of < 5% and some capability of performing unfolds for linear combinations of the two distributions without previous training. The network can perform unfolds at rates > 1 Hz, ideal for application to some high-repetition-rate systems.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 2480461

Report Number(s):: LA-UR--24-23901

Journal Information:: Review of Scientific Instruments, Journal Name: Review of Scientific Instruments Journal Issue: 8 Vol. 95; ISSN 0034-6748

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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