Journal Article

PUBDB-2024-04731

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Neural Networks for ID Gap Orbit DistortionCompensation in PETRA III

Veglia, B. (Corresponding author)DESY* ; Agapov, I.DESY* ; Keil, J.DESY*

2024
North-Holland Publ. Co. Amsterdam

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Please use a persistent id in citations: doi:10.1016/j.nima.2024.169934  doi:10.3204/PUBDB-2024-04731

Abstract: Undulators are used in storage rings to produce extremely brilliant synchrotron radiation. In the ideal case, a perfectly tuned undulator always has a first and second field integrals equal to zero. But, in practice, field integral changes during gap movements can never be avoided for real-life devices. As they significantly impact the circulating electron beam, there is the need to routinely compensate such effects. Deep Neural Networks can be used to predict the distortion in the closed orbit induced by the undulator gap variations on the circulating electron beam. In this contribution several current state-of-the-art deep learning algorithms were trained on measurements from PETRA III. The different architecture performances are then compared to identify the best model for the gap-induced distortion compensation.

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Contributing Institute(s):

1. Beschleunigerphysik (MPY)

Research Program(s):

1. 621 - Accelerator Research and Development (POF4-621) (POF4-621)
2. 6G3 - PETRA III (DESY) (POF4-6G3) (POF4-6G3)
3. ACCLAIM - Accelerating Science with Artificial Intelligence and Machine Learning (innovation pool) (ACCLAIM) (ACCLAIM)

Experiment(s):

1. Facility (machine) PETRA III

Appears in the scientific report 2024

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; Current Contents - Physical, Chemical and Earth Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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