As the repetition rates of high-intensity, ultrashort laser systems increase, diagnostics with matching data collection speeds must be developed. We have previously developed a spatiotemporal laser diagnostic, STRIPED FISH, which measures the complete laser electric field on a single shot. To provide rapid feedback, we now introduce a high-repetition-rate compatible adaptation of the STRIPED FISH retrieval algorithm which condenses the key electric field quantities into a handful of scalars for rapid assessment of the pulse’s first-order spatiotemporal distortions, and we validate this novel retrieval method with an experimental data trace.
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.
Application of deep learning to shaped, short-pulse laser-driven ion acceleration. Using a neural network as a universal approximator function, i.e., a surrogate model, we can map out large areas of parameter space. The neural network is informed by a large dataset of about 1,000, mid-fidelity particle-in-cell simulations modeling instances of Target-Normal Sheath Acceleration. The neural-network-based function allows us to rapidly explore regions of interest in search of optimal input parameters and features of interest.
As high-intensity short-pulse lasers that can operate at high-repetition-rate (HRR) (>10 Hz) come online around the world, the high-energy-density (HED) science they enable will experience a radical paradigm shift. The >10^3x increase in shot rate over today’s shot-per-hour drivers translates into dramatically faster data acquisition and more experiments, and thus the potential to significantly accelerate the advancement of HED science. We will present the vision and ongoing work to realize a HRR framework that allows for rapidly delivered optimal experiments by bringing together feedback laser control loops, high-throughput targetry and diagnostics, cognitive simulation, enhanced HED codes, and advanced data analytics.
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