A new integrated machine learning framework for advanced photoemission

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.

The LCLS-II Photoinjector Laser Infrastructure

“…While this paper addresses the primary, pivotal challenges of the LCLS-II facility, it's acknowledged that specific issues, such as UV beam quality, remain. Ongoing efforts, including exploring Four-Wave Mixing (FWM) architectures [87,[89][90] and the potential adoption of green beam irradiated photocathodes, aim to refine these aspects further. The development of the LCLS-II photoinjector not only contributes to advancing X-ray science but also forges new exploratory pathways across a broad scientific spectrum.…”

“…In order to generate the required amount of data for these ML studies, we have developed a startto-end software model of the photoinjector laser to explore the shaping parameter space [86,87]. Our current studies focus on tuning the software models to closely match the experimental system and developing ML-enhanced simulation techniques to speed up the data generation process [88].…”