Development of a deep learning based automated data analysis for step-filter x-ray spectrometers in support of high-repetition rate short-pulse laser-driven acceleration experiments

R A Simpson; D Mariscal; G J Williams; G G Scott; E Grace; T Ma

doi:10.1063/5.0043835

Development of a deep learning based automated data analysis for step-filter x-ray spectrometers in support of high-repetition rate short-pulse laser-driven acceleration experiments

Rev Sci Instrum. 2021 Jul 1;92(7):075101. doi: 10.1063/5.0043835.

Authors

R A Simpson¹, D Mariscal², G J Williams², G G Scott², E Grace², T Ma²

Affiliations

¹ Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
² Lawrence Livermore National Laboratory, Livermore, California 94550, USA.

PMID: 34340437
DOI: 10.1063/5.0043835

Abstract

We present a deep learning based framework for real-time analysis of a differential filter based x-ray spectrometer that is common on short-pulse laser experiments. The analysis framework was trained with a large repository of synthetic data to retrieve key experimental metrics, such as slope temperature. With traditional analysis methods, these quantities would have to be extracted from data using a time-intensive and manual analysis. This framework was developed for a specific diagnostic, but may be applicable to a wide variety of diagnostics common to laser experiments and thus will be especially crucial to the development of high-repetition rate (HRR) diagnostics for HRR laser systems that are coming online.