Machine Learning Helps Ease the Jitters of High-Power Lasers

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From left to right, Berkeley Lab researchers Anthony Gonsalves, Alessio Amodio, and Dan Wang align precision optics to prepare the Berkeley Lab Laser Accelerator (BELLA) Petawatt laser for laser-plasma accelerator (LPA) experiments. The machine learning–based control algorithm stabilizes the high-power laser’s pointing at the LPA target. (Credit: Thor Swift/Berkeley Lab)
(Left to Right) Anthony Gonsalves, Staff Scientist, Accelerator Technology and Applied Physics (ATAP), Alessio Amodio, Electronics Engineer, Engineering Division, and Dan Wang, Research Scientist, ATAP, discuss the setup of the PW Laser at the Berkeley Lab Laser Accelerator (BELLA) Center in Building 71 at Lawrence Berkeley National Laboratory (Berkeley Lab). The machine learning–based control algorithm stabilizes the high-power laser’s pointing at the LPA target (credit: Thor Swift/Berkeley Lab).

July 15, 2025 | Originally published by Lawrence Berkeley National Laboratory (Berkeley Lab) on June 10, 2025

Researchers at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) have made a breakthrough in laser technology by using machine learning (ML) to help stabilize a high-power laser.

This advancement, spearheaded by Berkeley Lab’s Accelerator Technology & Applied Physics (ATAP) and Engineering Divisions, promises to accelerate progress in physics, medicine, and energy. The researchers report their work in the journal High Power Laser Science and Engineering.

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