A new class of multi-kilojoule, multi-picosecond short-pulse lasers such as NIF-ARC, OMEGA-EP, LMJ-PETAL and LFEX-GEKKO, enable exciting opportunities to produce high-brightness, high-energy laser-driven particle sources for applications in high-energy density science. Recent results on these platforms have demonstrated enhanced accelerated proton energies and electron temperatures when compared to established scaling laws. Motivated by these results, this work examines laser-driven proton and electron acceleration in the multi-picosecond regime (>1ps) at laser intensities of 1017 – 1019 W/cm2. A detailed scaling study was performed on the TITAN laser at the Jupiter Laser Facility and found that the accelerated electrons and maximum proton energies exceeded the ponderomotive scaling in the multi-picosecond regime. The results are consistent with the accelerating sheath field being established by the superponderomotive electron population, rather than ponderomotive. A new analytical scaling is presented to reflect this enhancement in accelerated particle characteristics in this laser parameter regime.
In addition to the advancement of multi-kilojoule laser systems, high-intensity (>1018 W/cm2) short-pulse (<ps) lasers that operate at 1 Hz or faster are coming online around the world, opening up a myriad of opportunities for accelerating the rate of learning and expanding the reach of high-energy density (HED) science. In order to unlock these applications in HED science more completely, high repetition rate (HRR) diagnostics combined with real-time analysis tools must be developed to process experimental measurements and outputs at HRR. In this seminar, we also discuss two new research thrusts aimed at pushing forward the canonical problem of the optimization of short pulse laser acceleration of protons using machine learning. First, we present an automated data analysis framework based on machine learning for target diagnostics, such as an x-ray spectrometer, which is a common diagnostic on short pulse experiments. Then, we discuss briefly a proposed methodology based on representation learning to integrate heterogeneous data (such as proton and electron spectra) to constrain parameters that are not directly measurable such as the spatio-temporal evolution of the accelerating sheath field. Taken together, these thrusts enable a new preliminary framework for enhanced analysis of complex HRR, HED experiments and a foundational step towards realizing the goal of tunable laser-driven particle sources.
