Advancing adaptive optics towards machine learning for laser-driven experiments
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Shaping the field of a driving laser offers a powerful route to controlling highly nonlinear processes, such as high harmonic generation (HHG), and lies at the heart of coherent control techniques.听 Adaptive optics elements, such as deformable mirrors (DMs) or spatial light modulators (SLMs), together with optimization routines are often used to control the properties of laser fields.听 However, the large parameter space associated with these devices often makes conventional optimization methods slow to converge.听听
Future applications increasingly demand the rapid generation and characterization of laser beams with complex spatial and temporal profiles. As a result, there has been growing interest in applying machine learning techniques to adaptive optics [1].
In this talk, I will first discuss experiments to control the brightness of a HHG source using spatially shaped pulses [2].听听 I will then present our recent work combining adaptive optics with machine learning approaches, including neural networks and vision transformers, for image-based wavefront sensing [3].
[1] Guo et al. 鈥淎daptive optics based on machine learning: a review鈥 Opto-Electron Adv, 5, 200082 (2022)
[2] Treacher et al.听 鈥淚ncreasing the brightness of harmonic XUV radiation with spatially-tailored driver beams鈥.听 J. Opt. 23, 015502 (2021)
[3] O鈥橰ourke and O鈥橩eeffe. 鈥淒ual-plane wavefront sensing using a vision transformer鈥 Opt. Express, 34, 6456, (2026)