Researchers use machine learning to optimize high-power laser experiments

High intensity and high repetition lasers rapidly and continuously emit powerful bursts of light, capable of emitting multiple times per second. Commercial fusion energy factories and advanced compact radiation sources are common examples of systems that rely on such laser systems. However, humans are a major limiting factor as their response time is insufficient to manage such rapid shooting systems.

To address this challenge, scientists are searching for different ways to leverage the power of automation and artificial intelligence, which have real-time monitoring capabilities and can perform high-intensity operations.

A group of researchers from the Lawrence Livermore National Laboratory (LLNL), the Fraunhofer Laser Technology Institute (ILT), and the Aurora Infrastructure (ELI ERIC) are conducting an experiment at the ELI beamline facility in the Czech Republic to optimize high-power lasers using machine learning (ML).

Researchers trained LLNL's cognitive simulation development ML code on laser target interaction data, allowing researchers to adjust as the experiment progressed. The output is fed back to the ML optimizer, allowing it to fine tune the pulse shape in real time.

The laser experiment lasted for three weeks, each lasting about 12 hours. During this period, the laser fired 500 times at 5-second intervals. After every 120 shots, stop the laser to replace the copper target foil and check the vaporized target.

"Our goal is to demonstrate reliable diagnosis of laser accelerated ions and electrons from solid targets with high intensity and repeatability," said Matthew Hill, chief researcher at LLNL. "With the support of machine learning optimization algorithms' fast feedback to the laser front-end, the total ion yield of the system can be maximized."

Researchers have made significant progress in understanding the complex physics of laser plasma interactions using the most advanced high repetition rate advanced pulse laser system (L3-HAPLS) and innovative ML technology.

So far, researchers have relied on more traditional scientific methods, which require manual intervention and adjustment. With the help of machine learning capabilities, scientists are now able to analyze large datasets more accurately and make real-time adjustments during experiments.

The success of the experiment also highlights the ability of L3-HAPLS, L3-HAPLS is one of the most powerful and fastest high-intensity laser systems in the world. The experiment has proven that L3-HAPLS has excellent performance repeatability, focus quality, and extremely stable alignment.

Hill and his LLNL team spent about a year collaborating with the Fraunhofer ILT and ELI Beamlines teams to prepare for the experiment. The Livermore team utilized several new instruments developed under laboratory led research and development plans, including representative scintillation imaging systems and REPPS magnetic spectrometers.

The lengthy preparation work paid off as the experiment successfully generated reliable data that can serve as the foundation for progress in various fields including fusion energy, materials science, and medical treatment.

GenAI technology has always been at the forefront of scientific innovation and discovery. It is helping researchers break through the boundaries of scientific possibilities. Last week, researchers from MIT and the University of Basel in Switzerland developed a new machine learning framework to reveal new insights into materials science. Last week, artificial intelligence was proven to play an important role in drug discovery.

Source: Laser Net