Harnessing Machine Learning for Breakthroughs with High-Power Lasers
A team of international scientists from Lawrence Livermore National Laboratory (LLNL), Fraunhofer Institute for Laser Technology ILT, and the Extreme Light Infrastructure (ELI) recently collaborated on an experiment that has the potential to revolutionize the field of high-intensity high-repetition rate laser technology. By combining cutting-edge laser technology with machine learning techniques, the researchers were able to optimize the performance of high-power lasers in a way that had never been done before. This breakthrough experiment marks a significant advancement in the study, understanding, and practical application of high-intensity lasers.
Lead researcher Matthew Hill of LLNL shared that the primary objective of the experiment was to enhance the diagnosis of laser-accelerated ions and electrons from solid targets at high intensities and repetition rates. By leveraging a machine-learning optimization algorithm to provide rapid feedback to the laser front end, the team successfully maximized the total ion yield of the system. This achievement has opened up new possibilities for various fields, including medical therapy, materials science, and non-destructive analysis in cultural heritage and archaeology.
During the campaign, over 4000 shots were fired, consistently exceeding laser intensities of 3×10^21 W/cm² onto solid targets. The optimization of ion yield above the nominal baseline performance demonstrated the success of the experiment. Matthew Hill emphasized the importance of the high-quality and large volume of data produced through the experiment, underscoring the need to explore the underlying physics further.
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. The exceptional capabilities of the L3-HAPLS laser, including laser performance repeatability, precision, beam quality, and the generation of intense laser pulses at a high repetition rate, played a crucial role in the success of the experiment.
Constantin Haefner, Managing Director of Fraunhofer ILT and Director of the Chair for Laser Technology LLT at RWTH Aachen University, highlighted the collaborative effort as a remarkable endeavor to deepen the understanding of laser-plasma interactions. By utilizing the HAPLS laser and machine learning techniques, the research team embarked on a journey to unravel the complexities of laser-plasma interactions, showcasing the power of teamwork and technological advancements.
The successful integration of machine learning in target diagnostics and dispersion controls of a high-power, high-repetition-rate laser represents a significant milestone for both the ELI Beamlines Facility and the broader high energy density science community. The Chief Laser Scientist at ELI Beamlines, Bedrich Rus, lauded the cutting-edge quality and reliability of the L3-HAPLS laser system, emphasizing its role in enabling transformative experiments.
Overall, this collaborative effort between international research institutions, combining state-of-the-art laser technology with machine learning, has paved the way for groundbreaking advancements in the field of high-power lasers. The successful execution of the complex experiment at the ELI Beamlines Facility demonstrates the commitment to pushing the frontiers of scientific knowledge, showcasing the readiness and ability to redefine what’s possible in laser science and beyond.
