High-power lasers are vital for particle acceleration,imaging,fusion and materials processing,requiring precise control and high-energy delivery.Laser plasma accelerators(LPAs)demand laser positional stability at focu...High-power lasers are vital for particle acceleration,imaging,fusion and materials processing,requiring precise control and high-energy delivery.Laser plasma accelerators(LPAs)demand laser positional stability at focus to ensure consistent electron beams in applications such as X-ray free-electron lasers and high-energy colliders.Achieving this stability is especially challenging for the low-repetition-rate lasers in current LPAs.We present a machine learning method that predicts and corrects laser pointing instabilities in real-time using a high-frequency pilot beam.By preemptively adjusting a correction mirror,this approach overcomes traditional feedback limits.Demonstrated on the BELLA petawatt laser operating at the terawatt level(30 mJ amplification),our method achieved root mean square pointing stabilization of 0.34 and 0.59μrad in the x and y directions,reducing jitter by 65%and 47%,respectively.This is the first successful application of predictive control for shot-to-shot stabilization in low-repetition-rate laser systems,paving the way for full-energy petawatt lasers and transformative advances across science,industry and security.展开更多
基金supported by the Office of Science,Office of High Energy Physics,of the US Department of Energythe Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under contract No.DE-AC02-05CH11231。
文摘High-power lasers are vital for particle acceleration,imaging,fusion and materials processing,requiring precise control and high-energy delivery.Laser plasma accelerators(LPAs)demand laser positional stability at focus to ensure consistent electron beams in applications such as X-ray free-electron lasers and high-energy colliders.Achieving this stability is especially challenging for the low-repetition-rate lasers in current LPAs.We present a machine learning method that predicts and corrects laser pointing instabilities in real-time using a high-frequency pilot beam.By preemptively adjusting a correction mirror,this approach overcomes traditional feedback limits.Demonstrated on the BELLA petawatt laser operating at the terawatt level(30 mJ amplification),our method achieved root mean square pointing stabilization of 0.34 and 0.59μrad in the x and y directions,reducing jitter by 65%and 47%,respectively.This is the first successful application of predictive control for shot-to-shot stabilization in low-repetition-rate laser systems,paving the way for full-energy petawatt lasers and transformative advances across science,industry and security.
