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URLhttps://doi.org/10.18429/JACoW-IBIC2024-TUP54
TitleEstimation of beam transverse parameters through a multimode fiber using deep learning
Authors
  • Q. Xu, H. Zhang
    Cockcroft Institute
  • A. Hill, C. Welsch
    The University of Liverpool
  • F. Roncarolo, G. Trad
    European Organization for Nuclear Research
AbstractIn response to CERN's need for alternative imaging solutions of scintillating screens due to the discontinuation of radiation-hardened VIDICON tubes, the single large-core multimode fiber (MMF) has been identified as a potential medium to transmit image signals to a CMOS camera situated away from radiation-prone areas. However, significant challenges in image distortion at the fiber's output end complicate the reconstruction of the original beam distribution. To address this, a novel machine learning-based approach was introduced that utilizes a deep convolutional encoder-regressor network. It first compresses the fiber image into a latent space. Subsequently, a fully connected regression network directly estimates the beam parameters, such as centroids and widths, from the encoder output without the need to reconstruct the detailed image. This contribution will showcase an end-to-end system capable of estimating transverse beam parameters from the MMF output speckle patterns. Offering a safe, camera-preserving solution for beam imaging in high-radiation environments.
Paperdownload: TUP54.pdf
CiteBibTeX, LaTeX, Text/Word, RIS, EndNote
ConferenceProc. 13th International Beam Instrumentation Conference
Series
LocationBeijing
Date09-13 Sep 2024
PublisherJACoW Publishing, Geneva, Switzerland
Editorial BoardJianshe Cao (IHEP), Junhui Yue (IHEP), Lin Bian (IHEP), Ivan Andrian (Elettra-Sincrotrone Trieste S.C.p.A.), Volker RW Schaa (GSI), Zhichu Chen (SARI)
Online ISBN978-3-95450-249-3
Online ISSN2673-5350
Received05 September 2024
Revised08 September 2024
Accepted08 September 2024
Issued11 December 2024
DOI10.18429/JACoW-IBIC2024-TUP54
Pages170-173