Compensation of carrier envelope phase slip using machine learning
- Type
- Article
- Citation
- HIGH POWER LASER SCIENCE AND ENGINEERING, v.14, pp.1 - 7
- Issued Date
- 2026-03
- Abstract
- We demonstrate the effective compensation of carrier envelope phase (CEP) slip in a high-power femtosecond laser using a machine-learning (ML)-based control scheme. The compensation is achieved through fine dispersion tuning guided by a recurrent neural network (RNN) that predicts the temporal evolution of CEP slip, combined with a reinforcement-learning (RL) scheme that determines the optimal corrective actions. With this RNN+RL framework, the integrated CEP noise is reduced by more than a factor of two compared with a conventional proportional-integral-derivative controller. The proposed ML-based control methodology provides a versatile tool for stabilizing and optimizing various parameters in high-power laser systems.
- Publisher
- CAMBRIDGE UNIV PRESS
- ISSN
- 2095-4719
- Appears in Collections:
- Department of Physics and Photon Science > 1. Journal Articles
Research Institutes > 1. Journal Articles
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