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Research & Development

Machine learning harnessed to enhance high-power lasers

02 May 2024

LLNL, Fraunhofer ILT, and ELI partner to optimize high-intensity laser technology.

A team of international scientists from Lawrence Livermore National Laboratory (LLNL), Fraunhofer Institute for Laser Technology (ILT), and the Extreme Light Infrastructure (ELI) have collaborated on an experiment to optimise high-intensity high-repetition rate laser technology using machine learning.

The partners this week announced that the experiment “represents a significant leap forward in the study, understanding, and practical application of high-intensity lasers.”

Matthew Hill of LLNL, the lead researcher, said, “Our goal was to demonstrate robust diagnosis of laser-accelerated ions and electrons from solid targets at a high intensity and repetition rate. Supported by rapid feedback from a machine-learning optimisation algorithm to the laser front end, it was possible to maximise the total ion yield of the system.”

Advances in medicine, materials, and analysis

This collaborative effort and the utilisation of state-of-the-art laser technology coupled with machine learning techniques have opened new avenues for advancements in various fields such as medical therapy, materials science, and non-destructive analysis in the field of cultural heritage and archaeology.

Over 4000 shots fired during the campaign, which consistently exceeded laser intensities of 3x1021 W/cm2 onto solid targets, demonstrated optimization of ion yield above the nominal baseline performance. “The high quality and large volume of data that was produced and must now be worked with to explore the underlying physics validates the hard work of the entire team,” said Hill.

The experiment took place at the ELI Beamlines Facility in the Czech Republic, where the researchers utilised the state-of-the-art High-Repetition-Rate Advanced Petawatt Laser System (L3-HAPLS) to generate protons in the ELIMAIA Laser-Plasma Ion accelerator.

“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,” said Constantin Häfner, Managing Director of Fraunhofer ILT and Director of the Chair for Laser Technology LLT at RWTH Aachen University. “This collaborative effort serves as a testament to the strength of teamwork and technological advancements in pushing the boundaries of scientific knowledge together.”

Demonstrating the integration of machine learning between target diagnostics and the dispersion controls of a high-power, high-repetition-rate laser is a significant milestone both for the facility and the wider high energy density science community.

“The successful execution of such a complex experiment showcases the cutting-edge quality and reliability of the L3-HAPLS laser system,” said Bedrich Rus, Chief Laser Scientist at ELI Beamlines. Daniele Margarone, Director of Research and Operations of ELI Beamlines concludes, “With such experiments ELI demonstrates the readiness and ability to pushing the frontiers of knowledge. We at ELI are committed to enable transformative experiments that redefine what's possible in laser science and beyond.”

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