Beam-pointing drift prediction in pulsed lasers by a probabilistic learning approach

Area of Science:

• Laser Physics and Optics
• Machine Learning Applications

Background:

• Laser beam pointing stability is crucial but affected by un-modeled factors like vibrations and air disturbances.
• Existing methods struggle with the time-varying nature of beam-pointing shifts and lack generalizability across different laser systems.

Purpose of the Study:

• To develop a generic and effective approach for predicting laser beam-pointing drift.
• To utilize a probabilistic learning method for modeling and predicting beam-pointing errors.

Main Methods:

• Employed Gaussian mixture models to estimate the joint distribution of time and shifting errors from sampled laser system data.
• Applied Gaussian mixture regression for predicting beam-pointing errors at arbitrary future times.
• Validated the approach on a pulsed Nd:YAG laser system operating at 1064 nm and 100 Hz.

Main Results:

• The proposed Gaussian mixture model-based approach accurately predicted beam-pointing drift in the tested laser system.
• Demonstrated remarkable performance in capturing and forecasting beam-pointing dynamics.
• Showcased the potential for a generic solution applicable to various laser systems.

Conclusions:

• Probabilistic learning, specifically Gaussian mixture models, offers a powerful alternative to physical modeling for beam-pointing drift prediction.
• The developed method provides a robust and generalizable solution for enhancing laser system stability.
• This approach significantly advances the ability to manage and compensate for beam-pointing errors in real-world laser applications.