A new integrated machine learning framework for advanced photoemission

Hao Zhang; Jack Hirschman; Randy Lemons; Linshan Sun; Brittany Lu; Sergio Carbajo

doi:10.1117/12.2676782

26 September 2023 A new integrated machine learning framework for advanced photoemission

Hao Zhang, Jack Hirschman, Randy Lemons, Linshan Sun, Brittany Lu, Sergio Carbajo

Proceedings Volume 12667, Laser Beam Shaping XXIII; 126670F (2023) https://doi.org/10.1117/12.2676782
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Conference Poster

Abstract

The next generation of ultra-bright photoemission sources may offer opportunities to enhance our understanding of fundamental spatiotemporal scales. However, modeling photoemission and laser shaping systems precisely and efficiently is difficult due to the numerous interdependent linear and nonlinear processes involved and significant variations in modeling frameworks. Here, we present a new machine learning-based framework for photoemission laser systems and dynamic laser shaping. To showcase the effectiveness of our approach in system optimization, reverse engineering, and design. Our framework is designed to facilitate precise adaptive temporal shaping, including the generation of longitudinally flat-top or periodically-modulated pulses, through integration with four-wave mixing.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hao Zhang, Jack Hirschman, Randy Lemons, Linshan Sun, Brittany Lu, and Sergio Carbajo "A new integrated machine learning framework for advanced photoemission", Proc. SPIE 12667, Laser Beam Shaping XXIII, 126670F (26 September 2023); https://doi.org/10.1117/12.2676782

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