High-intensity, high-repetition lasers emit powerful bursts of light in rapid succession and can be fired multiple times per second. Commercial fusion energy plants and modern compact radiation sources are common examples of systems that rely on such laser systems. However, humans are a major limiting factor because human response time is insufficient to manage such rapid-fire systems.
To address this challenge, scientists are exploring different ways to harness the power of automation and artificial intelligence with real-time monitoring capabilities for high-intensity tasks.
A team of researchers from Lawrence Livermore National Laboratory (LLNL), Fraunhofer Institute for Laser Technology (ILT), and Extreme Light Infrastructure (ELI ERIC) conduct experiments to optimize high-power lasers at the ELI Beamline facility in the Czech Republic. We are doing Use machine learning (ML).
The researchers trained the ML code developed by LLNL's cognitive simulations on laser-target interaction data so that it could be adjusted as the experiment progressed. The output is fed back to the ML optimizer, allowing fine-tuning of the pulse shape in real time.
The laser experiments were conducted over a three-week period, with each experiment lasting approximately 12 hours, during which the laser was fired 500 times at 5-second intervals. After every 120 exposures, the laser was stopped, the copper target foil was replaced, and the evaporated target was inspected.
“Our goal was to demonstrate robust diagnostics of laser-accelerated ions and electrons from solid-state targets at high intensities and repetition rates,” said Matthew Hill, principal investigator at LLNL. . “Supported by rapid feedback from the machine learning optimization algorithm to the laser front end, we were able to maximize the total ion yield of the system.”
Researchers are harnessing the power of the cutting-edge High Repetition Rate Advanced Petawatt Laser System (L3-HAPLS) and innovative ML techniques to make significant advances in understanding the complex physics of laser-plasma interactions. We have made progress.
Until now, researchers have relied on more traditional scientific methods that require manual intervention and adjustments. ML capabilities have enabled scientists to more accurately analyze large data sets and make real-time adjustments while experiments are running.
(Nico El Nino/Shutterstock)
The success of this experiment also highlights the capabilities of L3-HAPLS, one of the most powerful and fastest high-brightness laser systems in the world. This experiment demonstrated the excellent performance reproducibility, focal spot quality, and highly stable alignment of L3-HAPLS.
Hill and the LLNL team, working with the Fraunhofer ILT and ELI beamline teams, spent about a year preparing the experiment, and the Livermore team used several new instruments developed through the Laboratory Directed Research and Development Program, including a repeat-rated scintillator imaging system and the REPPS magnetic spectrometer.
Long preparations paid off in this experiment, which successfully generated robust data that will serve as the basis for advances in fields as diverse as fusion energy, materials science, and medicine.
GenAI technology has been at the forefront of scientific innovation and discovery, helping researchers push the boundaries of what is scientifically possible. Last week, researchers from MIT and the University of Basel in Switzerland announced New machine learning framework Uncover new insights into materials science. Last week, AI proved that: Very useful for drug discovery.
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