International Collaboration Uses Machine Learning to Optimize High-Intensity Laser Experiments at L3-HAPLS Facility

International Team Uses Machine Learning to Optimize High-Intensity Laser Experiment

An international team of scientists from Lawrence Livermore National Laboratory (LLNL), Fraunhofer Institute for Laser Technology (ILT), and the Extreme Light Infrastructure (ELI ERIC) recently collaborated on an experiment to optimize a high-intensity, high-repetition-rate laser using machine learning. The goal of the experiment was to demonstrate robust diagnosis of laser-accelerated ions and electrons from solid targets at a high intensity and repetition rate.

Lead researcher Matthew Hill from LLNL stated, “Supported by rapid feedback from a machine-learning optimization algorithm to the laser front end, it was possible to maximize the total ion yield of the system.” The team trained a closed-loop machine learning code on laser-target interaction data to optimize the laser pulse shape, allowing it to make adjustments in real-time as the experiment ran.

The experiment took place at the ELI Beamlines Facility in the Czech Republic, where the researchers utilized the state-of-the-art High-Repetition-Rate Advanced Petawatt Laser System (L3-HAPLS) to generate protons in the ELIMAIA laser-plasma ion accelerator. More than 4,000 shots were fired during the campaign, allowing for statistical analysis and demonstrating optimization of ion yield above the nominal baseline performance.

Constantin Haefner, managing director of Fraunhofer ILT, commented, “By harnessing the HAPLS and pioneering machine learning techniques, we embarked on a remarkable endeavor to further comprehend the intricate physics of laser-plasma interactions.”

The successful execution of the experiment showcased the cutting-edge quality and reliability of the L3-HAPLS laser system. LLNL developed the HAPLS laser as part of a bilateral agreement with ELI Beamlines, with first light from the system after delivery and installation in the Czech Republic in 2017.

The LLNL team, along with collaborators from Fraunhofer ILT and ELI Beamlines, spent about a year preparing for the experiment. They utilized various instruments developed under the Laboratory Directed Research and Development Program, including the REPPS magnetic spectrometer, PROBIES ion beam imaging spectrometer, and rep-rated X-ray spectrometer.

Overall, the use of machine learning in this high-intensity laser experiment proved to be a successful and innovative approach, paving the way for further advancements in the field of laser technology and plasma interactions.