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000599237 005__ 20240105213206.0
000599237 037__ $$aPUBDB-2023-07244
000599237 041__ $$aEnglish
000599237 1001_ $$0P:(DE-H253)PIP1088102$$aTuennermann, Henrik$$b0$$eCorresponding author$$udesy
000599237 1112_ $$aPhotonics West / LASE 2023$$cSan Francisco$$d2023-01-28 - 2023-02-02$$wUSA
000599237 245__ $$aMachine Learning Enhanced Beam Pointing Stabilization for the Pump-Probe Laser at the FLASH FEL Facility
000599237 260__ $$c2023
000599237 3367_ $$033$$2EndNote$$aConference Paper
000599237 3367_ $$2DataCite$$aOther
000599237 3367_ $$2BibTeX$$aINPROCEEDINGS
000599237 3367_ $$2DRIVER$$aconferenceObject
000599237 3367_ $$2ORCID$$aLECTURE_SPEECH
000599237 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1704444618_3625741
000599237 520__ $$aPump-probe lasers for FELs must provide stable pulse energy, timing, and beam position. Here, we show active stabilization of beam pointing fluctuations using a combination of classic control, artificial intelligence, and machine learning techniques. As our laser system operates in 10 Hz burst mode, fast feedback is not possible. Therefore, we have to utilize the available information as efficiently as possible. Beam pointing fluctuations of laser beams can be described by 4 parameters – as the actuators (motorized mirrors) are not orthogonal we need a model to calculate the required actuator movements. As effects such as motor acceleration are not easy to capture in a physical model, we use an automated data-driven approach. The measurement of the beam position is noisy, so we use a Kalman-Filter, which also integrates our feedback actions to smooth the output. Finally, we use an integrating controller to control the beam. The final transport of the beam to the pump-probe experiment introduces additional drifts, but during user operation, the beam position at the interaction point cannot be measured. We, therefore, measure correlated properties such as temperature, humidity, and air pressure and trained a machine learning model to predict its location. Integrating this model in a feed-forward loop could improve the RMS error of the beam position by 63% in the x-axis and 8% in the y-axis.
000599237 536__ $$0G:(DE-HGF)POF4-6G2$$a6G2 - FLASH (DESY) (POF4-6G2)$$cPOF4-6G2$$fPOF IV$$x0
000599237 536__ $$0G:(DE-HGF)2020_InternLabs-0011$$aInternLabs-0011 - HIR3X - Helmholtz International Laboratory on Reliability, Repetition, Results at the most advanced X-ray Sources (2020_InternLabs-0011)$$c2020_InternLabs-0011$$x1
000599237 693__ $$0EXP:(DE-H253)F-PG1-20150101$$1EXP:(DE-H253)FLASH-20150101$$6EXP:(DE-H253)F-PG1-20150101$$aFLASH$$fFLASH Beamline PG1$$x0
000599237 693__ $$0EXP:(DE-H253)FLASH2020p-20221201$$1EXP:(DE-H253)FLASHII-20150901$$5EXP:(DE-H253)FLASH2020p-20221201$$aFLASH II$$eFLASH 2020+ Project$$x1
000599237 7001_ $$0P:(DE-H253)PIP1027495$$aPressacco, Federico$$b1$$udesy
000599237 7001_ $$0P:(DE-H253)PIP1087122$$aTavakol, Hamed$$b2$$udesy
000599237 7001_ $$0P:(DE-H253)PIP1018794$$aHartl, Ingmar$$b3$$udesy
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000599237 9101_ $$0I:(DE-588b)2008985-5$$6P:(DE-H253)PIP1088102$$aDeutsches Elektronen-Synchrotron$$b0$$kDESY
000599237 9101_ $$0I:(DE-588b)2008985-5$$6P:(DE-H253)PIP1027495$$aDeutsches Elektronen-Synchrotron$$b1$$kDESY
000599237 9101_ $$0I:(DE-588)1043621512$$6P:(DE-H253)PIP1027495$$aEuropean XFEL$$b1$$kXFEL.EU
000599237 9101_ $$0I:(DE-588b)2008985-5$$6P:(DE-H253)PIP1087122$$aDeutsches Elektronen-Synchrotron$$b2$$kDESY
000599237 9101_ $$0I:(DE-588b)2008985-5$$6P:(DE-H253)PIP1018794$$aDeutsches Elektronen-Synchrotron$$b3$$kDESY
000599237 9131_ $$0G:(DE-HGF)POF4-6G2$$1G:(DE-HGF)POF4-6G0$$2G:(DE-HGF)POF4-600$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bForschungsbereich Materie$$lGroßgeräte: Materie$$vFLASH (DESY)$$x0
000599237 9141_ $$y2023
000599237 9201_ $$0I:(DE-H253)FS-LA-20130416$$kFS-LA$$lLaser Forschung und Entwicklung$$x0
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000599237 980__ $$aI:(DE-H253)FS-LA-20130416
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