Rapid machine learning-based diagnostic analysis for high-energy-density experiments on high repetition rate laser systems

Raspberry Simpson; Derek Mariscal; Jackson Williams; Elizabeth Grace; Graeme G Scott; Kelly Swanson; Blagoje Djordjevic; Tammy Ma

doi:10.1117/12.2588285

5 March 2021 Rapid machine learning-based diagnostic analysis for high-energy-density experiments on high repetition rate laser systems

Raspberry Simpson, Derek Mariscal, Jackson Williams, Elizabeth Grace, Graeme G Scott, Kelly Swanson, Blagoje Djordjevic, Tammy Ma

Author Affiliations +

Proceedings Volume 11666, High Power Lasers for Fusion Research VI; 116660F (2021) https://doi.org/10.1117/12.2588285
Event: SPIE LASE, 2021, Online Only

Abstract

High intensity, high-repetition rate (HRR) lasers, that is lasers that can operate on the order of 1 Hz or faster, are quickly coming on-line around the world. High intensity lasers have long been an impactful tool in high energy density (HED) science since they are capable of creating matter at extreme temperatures and pressures relevant to this field. The advent of HRR technology enhances to this capability since HRR enables these types of these experiments to be performed faster, thus leading to an acceleration in the rate of learning in fundamental HED science. However, in order to use the full potential of HRR systems, high repetition rate diagnostics in addition to real-time analysis tools must be developed to process experimental measurements and outputs at a rate that matches the laser. Towards this goal, we present an automated machine learning based analysis for a synthetic X-ray spectrometer, which is a common diagnostic in HED experiments.

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Citation Download Citation

Raspberry Simpson, Derek Mariscal, Jackson Williams, Elizabeth Grace, Graeme G Scott, Kelly Swanson, Blagoje Djordjevic, and Tammy Ma "Rapid machine learning-based diagnostic analysis for high-energy-density experiments on high repetition rate laser systems", Proc. SPIE 11666, High Power Lasers for Fusion Research VI, 116660F (5 March 2021); https://doi.org/10.1117/12.2588285

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