Machine Learning based Pointing Stabilization for Low-Repetition-Rate High Power Lasers

Q: What type of study is this?

This is a Experimental Study study.

Alessio Amodio; Dan Wang; Curtis J. Berger; Hai-En Tsai; Samuel K. Barber; Jeroen van Tilborg; A. Picksley; Z. Eisentraut; Neel Rajeshbhai Vora; Mahek Logantha; Qing Ji; Qiang Du; Russell Wilcox; A. J. Gonsalves

doi:10.1364/cleo_at.2025.aa113_5

January 1, 2025

Machine Learning based Pointing Stabilization for Low-Repetition-Rate High Power Lasers

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Key Points

The system achieved sub-µrad stability, a significant improvement in laser pointing accuracy under challenging conditions.
This predictive control method resolves traditional feedback system bandwidth limitations for high-power laser applications.
Testing at the BELLA Center demonstrates promising results for enhancing laser performance in low-repetition-rate environments.
The machine learning approach innovatively addresses spatial transverse stability, paving the way for future advancements in laser technology.

Implication

Novel closed-loop control system achieves sub-µrad stability in high-power lasers, indicating predictive control potential.

Abstract

A novel machine-learning based closed-loop control system to enhance spatial transverse stability of high-power lasers, overcoming the bandwidth limitations of traditional feedback systems. Tested at the BELLA Center, it achieved sub- µrad stability, marking the first successful predictive control in high-power low-repetition-rate facility.

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Cite This Study

Amodio et al. (2025) studied this question.

synapsesocial.com/papers/68d464f131b076d99fa6440b https://doi.org/10.1364/cleo_at.2025.aa113_5

View Full Paper

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Machine Learning based Pointing Stabilization for Low-Repetition-Rate High Power Lasers

Key Points

Implication

Abstract

Discussion

Cite This Study

Machine Learning based Pointing Stabilization for Low-Repetition-Rate High Power Lasers

Key Points

Implication

Abstract

Discussion

Cite This Study

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