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DOE OSTI · 3375096

Application of machine learning techniques for fast MeV x-ray spectra unfolding from filter stack spectrometer data

Alvarez, Mariana Alvarado [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0009000783586711)·Wong, Chun-Shang [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000226987208)·Wolfe, Bradley Thomas [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000268301614)·Bogale, Alemayehu S. [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Univ. of California, San Diego, CA (United States). Center for Energy Research] (ORCID:0000000306011512)·Broughton, David Paul [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000347008133)·Huang, Chengkun [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000231768042)·Reinovsky, Robert Emil [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)]·Wang, Zhehui [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000178264063)·Batha, Steven H. [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000273956527)
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Abstract

Recovery of MeV x-ray spectra from detector signals is difficult because the response matrix inversion is ill-conditioned and current methods are too slow for high-repetition-rate experiments. In this work, we make use of neural networks to unfold MeV x-ray spectra from measurements obtained with a filter stack spectrometer at rates of near 40 Hz. The neural network was trained on synthetic data and tested on both synthetic and experimental data, the latter obtained in two separate experiments performed at the Omega EP laser facility. We show here that this unfolding method has good performance on synthetic data and that it is a promising option for experimental data of up to 40 MeV. The accuracy on experimental data is verified by using a simple forward model to compare against measured values.

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Alvarez, Mariana Alvarado [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0009000783586711), Wong, Chun-Shang [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000226987208), Wolfe, Bradley Thomas [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000268301614), Bogale, Alemayehu S. [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Univ. of California, San Diego, CA (United States). Center for Energy Research] (ORCID:0000000306011512), Broughton, David Paul [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000347008133), Huang, Chengkun [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000231768042), Reinovsky, Robert Emil [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)], Wang, Zhehui [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000178264063), Batha, Steven H. [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000273956527). 2026-03-23. Application of machine learning techniques for fast MeV x-ray spectra unfolding from filter stack spectrometer data. https://doi.org/10.1063/5.0311185

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