Real-time machine-learning-driven control system of a deformable mirror for achieving aberration-free X-ray wavefronts

Author:

Rebuffi LucaORCID,Shi XianboORCID,Qiao ZhiORCID,Highland Matthew J.,Frith Matthew G.,Wojdyla Antoine1ORCID,Goldberg Kenneth A.1ORCID,Assoufid Lahsen

Affiliation:

1. Lawrence Berkeley National Laboratory

Abstract

A neural-network machine learning model is developed to control a bimorph adaptive mirror to achieve and preserve aberration-free coherent X-ray wavefronts at synchrotron radiation and free electron laser beamlines. The controller is trained on a mirror actuator response directly measured at a beamline with a real-time single-shot wavefront sensor, which uses a coded mask and wavelet-transform analysis. The system has been successfully tested on a bimorph deformable mirror at the 28-ID IDEA beamline of the Advanced Photon Source at Argonne National Laboratory. It achieved a response time of a few seconds and maintained desired wavefront shapes (e.g., a spherical wavefront) with sub-wavelength accuracy at 20 keV of X-ray energy. This result is significantly better than what can be obtained using a linear model of the mirror’s response. The developed system has not been tailored to a specific mirror and can be applied, in principle, to different kinds of bending mechanisms and actuators.

Funder

Department of Energy, Office of Science, Office of Basic Energy Sciences

Publisher

Optica Publishing Group

Subject

Atomic and Molecular Physics, and Optics

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