Researchers use machine learning to optimize high-power laser experiments

High-intensity and high-repetition lasers can emit powerful light multiple times per second in rapid succession. Commercial fusion energy plants and advanced fuel-based radiation sources rely on such lasers. However, human reaction time is insufficient to manage such rapid-fire systems, making application challenging.

To address this challenge, scientists are looking for different ways to harness the power of automation and artificial intelligence, technologies with high-intensity operations Real-time monitoring capabilities.

A team of researchers from Lawrence Livermore National Laboratory (LLNL), Fraunhofer Institute for Laser Technology (ILT) and Aurora Infrastructure (ELI ERIC) An experiment is underway in the Czech Republic to use machine learning (ML) to optimize high-power lasers. Their goal is to improve the efficiency of lasers for better applications in scientific research and engineering technology. The research was designed to address a key issue in current laser technology, which is that lasers tend to burn out when outputting at high powers. The researchers trained a cognitive simulation developed by LLNL. Machine learning code, which is based on laser-target interaction data, allows researchers to adjust as the experiment progresses. 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 experiment lasted about 12 hours, in which the laser was fired 500 times with an interval of 5 seconds. After every 120 shots, stop the laser to replace the copper target and check the roughened target.

Our goal is to demonstrate reliable diagnostics of laser-accelerated ions and electrons on solid targets with high intensity and repeatability, said Matthew Hill, principal investigator at LLNL. In optimization from machine learning Supported by fast feedback from the algorithm to the laser front end, the total ion yield of the system can be maximized using the state-of-the-art high repetition rate Advanced Petawatt Laser System (L3-HAPLS). ) and innovative machine learning techniques, researchers have made significant progress in understanding the complex physics of laser-plasma interactions. Aspects of this complex physics include laser particle acceleration, plasma dynamics and high energy density physics. Through these advances, we are able to understand and explore the details of complex physical systems in greater depth. This is important for

Until now, researchers have relied on traditional scientific methods, which require human intervention and adjustments. With machine learning capabilities, scientists can more accurately analyze large data sets and make real-time adjustments as experiments take place.

L3-HAPLS is one of the most powerful and fastest high-intensity laser systems in the world. Experiments have proven that L3-HAPLS has excellent performance, good repeatability, good caustic quality, and good alignment. Experiments have demonstrated the capabilities of L3-HAPLS, proving that it can be applied in multiple fields, such as materials processing, medical research, and scientific research. This laser system has the characteristics of high energy, high power and high repetition rate, bringing new breakthroughs to the development of laser technology. L3-HAPLS

Hill and his LLNL team, working with the Fraunhofer ILT and ELI teams, spent about a year preparing the experiment. The team used several new instruments developed by the Laboratory Directed Research and Development Program, including a reproducible scintillator imaging system and a REPPS magnetic spectrometer.

The lengthy preparations paid off, and the experiment successfully produced powerful data that can serve as the basis for developments in various fields including fusion energy, materials science, and medical treatments.

Generative artificial intelligence technology has always been at the forefront of scientific innovation and discovery. It is helping researchers push the boundaries of scientific possibility. For example, last week researchers from MIT and the University of Basel in Switzerland developed a new machine learning framework to reveal new insights in materials science. And artificial intelligence is proving to play an important role in drug discovery.

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