[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128527-en":3,"doc-seo-128527-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128527,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Real-time machine-learning-driven control system of a deformable mirror for achieving aberration-free X-ray wavefronts","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 using a mirror actuator response directly measured at a beamline with a real-time single-shot wavefront sensor based on a coded mask and wavelet-transform analysis. Testing on a bimorph deformable mirror at the 28-ID IDEA beamline achieves few-second response time and sub-wavelength accuracy at 20 keV, outperforming a linear model and generalizing across different mirror bending mechanisms.","Lawrence Berkeley National Laboratory LBL Publications  \nTitle  \nReal-time machine-learning-driven control system of a deformable mirror for achieving aberration-free X-ray wavefronts.  \nPermalink  \n[https://escholarship.org/uc/item/6fx7w42m](https://escholarship.org/uc/item/6fx7w42m)  \nJournal  \nOptics Express, 31(13)  \nISSN  \n1094-4087  \nAuthors  \nRebuffi, Luca  \nShi, Xianbo Qiao, Zhi et al.  \nPublication Date  \n2023-06-19  \nDOI  \n10.1364/oe.488189  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons AttributionNonCommercial License, available at [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nResearch Article  \nVol. 31, No. 13/19 Jun 2023/Optics Express 21264  \nReal-time machine-learning-driven control system of a deformable mirror for achieving aberration-free X-ray wavefronts  \nLUCA REBUFFI , 1,*  XIANBO SHI , 1  ZHI QIAO, 1  MATTHEW J. HIGHLAND, 1 MATTHEW G. FRITH , 1 ANTOINE WOJDYLA , 2   \nKENNETH A. GOLDBERG , 2  AND LAHSEN ASSOUFID1  \n1 Argonne National Laboratory, 9700 S CassAve, Lemont, IL 60439, USA  \n2 Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA 94720, USA  \n* [lrebuffi@anl.gov](lrebuffi@anl.gov)  \nAbstract: 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.  \n© 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement  \n1. Introduction  \nAchieving and maintaining high-intensity focused X-ray beams with near-perfect wavefront quality and high stability is the main challenge and prerequisite for experiments at 4th-generation synchrotron radiation and free electron laser (FEL) beamlines. This represents the main challenge for X-ray optical elements, which will necessarily have much more demanding specifications than those for other applications because of the shorter wavelength and the ultra-small emittance of the radiation beams generated by these sources. When using coherent photons from diffractionlimited light sources, it is critical to maintain a well-controlled wavefront and suppress unwanted static distortions and dynamic disturbance [1] . Not only can the degradation of the wavefront be detrimental for phase-sensitive imaging techniques like tomography [2], but wavefront uniformity is of particular importance for coherent X-ray scattering experiments using X-ray Photon Correlation Spectroscopy, Coherent Surface Scattering Imaging, and Coherent X-ray Diffraction Imaging techniques. Wavefront distortions degrade the sample speckle contrast, which can hinder data interpretation [3] and lead to the failure of the sample phase retrieval and reconstruction. Therefore, the optical elements must i) be manufactured with a surface figure closely following an ideal mathematical shape, ii) automatically and repeatably align and focus the beam to match different sample and experiment requirements, and iii) provide real-t","cbCaidU17z3cxDBk","https://ap.wps.com/l/cbCaidU17z3cxDBk","pdf",6671700,1,17,"English","en",105,"# Introduction\n## Adaptive optics challenge in coherent X-ray beamlines\n## Limitations of linear, open-loop deformable mirror control\n# Methods and approach\n## Neural-network controller for bimorph adaptive mirrors\n## Real-time single-shot wavefront sensing and training data\n# Experimental validation\n## Beamline testing results at the 28-ID IDEA beamline\n## Response time, wavefront fidelity, and comparison to linear models","[{\"question\":\"What problem does the proposed control system address?\",\"answer\":\"It targets the challenge of achieving and maintaining high-quality, stable, aberration-free coherent X-ray wavefronts for synchrotron and FEL experiments.\"},{\"question\":\"How is the neural-network controller trained?\",\"answer\":\"It is trained on beamline-measured actuator response data using a real-time single-shot wavefront sensor that combines a coded mask with wavelet-transform analysis.\"},{\"question\":\"What performance was demonstrated in the beamline experiment?\",\"answer\":\"At the 28-ID IDEA beamline, the system achieved a response time of a few seconds and maintained desired wavefront shapes with sub-wavelength accuracy at 20 keV, outperforming a linear response model.\"}]","Real-time machine-learning-driven control system of a deformable mirror for achieving aberration-free X-ray wavefronts | 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problem does the proposed control system address?","Question",{"text":76,"@type":77},"It targets the challenge of achieving and maintaining high-quality, stable, aberration-free coherent X-ray wavefronts for synchrotron and FEL experiments.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the neural-network controller trained?",{"text":81,"@type":77},"It is trained on beamline-measured actuator response data using a real-time single-shot wavefront sensor that combines a coded mask with wavelet-transform analysis.",{"name":83,"@type":74,"acceptedAnswer":84},"What performance was demonstrated in the beamline experiment?",{"text":85,"@type":77},"At the 28-ID IDEA beamline, the system achieved a response time of a few seconds and maintained desired wavefront shapes with sub-wavelength accuracy at 20 keV, outperforming a linear response 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