- Physical Review Journals
All JournalsPhysics Magazine

Physical Review Accelerators and Beams

Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access

Development of radiation-tolerant beam imaging via multimode fiber and synthetic data-driven machine learning

Q. Xu1,2,3,*, H. D. Zhang1,2,†, G. Trad3, A. Hill1,2, F. Roncarolo3, and C. P. Welsch1,2

  • *Contact author: qiyuan.xu@liverpool.ac.uk
  • †Contact author: haozhang@liverpool.ac.uk
PDF

Phys. Rev. Accel. Beams 29, 032801 – Published 9 March, 2026

DOI: https://doi.org/10.1103/wddb-m37l

Abstract

Transverse beam profile monitoring in high-radiation areas is challenging due to camera degradation. A proposed solution employs a multimode fiber (MMF) to relay optical signals from the radiation zone to a shielded area, where a standard CMOS camera can operate safely. However, MMF transmission introduces significant distortions, producing complex speckle patterns at the output. We present a machine-learning–based method to reconstruct the transverse beam distributions from these patterns. The model was trained solely on synthetic data generated via stochastic Gaussian mixture simulations, in which the samples were displayed on a laser-illuminated digital micromirror device (DMD) and relayed through a 5-m MMF. The same setup was used for testing, with beam images on a scintillating screen from CERN’s CLEAR facility replayed on the DMD. The model achieved a 1.49% mean relative root mean square error across four key beam parameters—77% lower than the 6.59% baseline, demonstrating its potential for diagnostics in radiation-constrained environments.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (41)

  1. E. Bravin, Transverse beam profiles, arXiv:2005.07400.
  2. P. Forck, Lecture Notes on Beam Instrumentation and Diagnostics (Joint University Accelerator School, Archamps, 2010), https://www-bd.gsi.de/conf/juas/juas_script.pdf.
  3. M. Brugger, Radiation damage to electronics at the LHC, in Proceedings of IPAC2012, New Orleans, Louisiana, USA (JACoW, Geneva, Switzerland, 2012), Vol. 1205201, THPPP006.
  4. D. Celeste, E. Bravin, S. Burger, F. Roncarolo et al., Development and test of a beam imaging system based on radiation tolerant optical fiber bundles, in Proceedings of the 10th International Particle Accelerator Conference, IPAC-2019, Melbourne, Australia (JACoW, Geneva, Switzerland, 2019), pp. 2658–2660.
  5. S. Burger, E. Bravin, A. Topaloudis, E. Veyrunes, F. Velotti, and F. Roncarolo, New CERN SPS beam dump imaging system, in Proceedings of the 10th International Beam Instrumentation Conference, IBIC-2021, Pohang, Republic of Korea (JACoW, Geneva, Switzerland, 2021), p. 254.
  6. Institut Supérieur de l’Aéronautique et de l’Espace, ISAE-SUPAERO, https://www.isae-supaero.fr/en/ (2023).
  7. F. Roncarolo, S. Burger, G. Trad, F. Velotti, A. Topaloudis, T. Levens, A. Guerrero, and T. Lefevre, Review of CERN beam instrumentation for fixed target experiments, in Proceedings of the 14th International Particle Accelerator Conference, Venice, Italy (JACoW, Geneva, Switzerland, 2023), THPL080.
  8. CERN Radiation Working Group, Beam profile measurements using beam camera at the LHC, Available at https://indico.cern.ch/event/1207408/contributions/5078453/attachments/2544392/4381289/RadWG_BeamCam_2022_11_09.pdf (2022) [accessed April 25, 2023].
  9. J. Blanc, F. Achten, A. Alessi, A. Amezcua, J. Kuhnhenn, A. Pastouret, D. Ricci, and I. Toccafondo, Characterization of radiation-resistant multimode optical fibers for large-scale procurement, IEEE Trans. Nucl. Sci. 68, 1407 (2021).
  10. S. Girard, J. Kuhnhenn, A. Gusarov, B. Brichard, M. Van Uffelen, Y. Ouerdane, A. Boukenter, and C. Marcandella, Radiation effects on silica-based optical fibers: Recent advances and future challenges, IEEE Trans. Nucl. Sci. 60, 2015 (2013).
  11. C. Campanella, V. De Michele, A. Morana, G. Mélin, T. Robin, E. Marin, Y. Ouerdane, A. Boukenter, and S. Girard, Radiation effects on pure-silica multimode optical fibers in the visible and near-infrared domains: Influence of OH groups, Appl. Sci. 11, 2991 (2021).
  12. D. L. Smith, L. V. Nguyen, M. I. Reja, E. P. Schartner, H. Ebendorff-Heidepriem, D. J. Ottaway, and S. C. Warren-Smith, Harnessing the power of complex light propagation in multimode fibers for spatially resolved sensing, Photonics Res. 12, 411 (2024).
  13. R. Paschotta, V number, RP photonics encyclopedia, available online at https://www.rp-photonics.com/v_number.html (2024).
  14. D. Psaltis and C. Moser, Imaging with multimode fibers, Opt. Photonics News 27, 24 (2016).
  15. W. Xiong, P. Ambichl, Y. Bromberg, B. Redding, S. Rotter, and H. Cao, Principal modes in multimode fibers: Exploring the crossover from weak to strong mode coupling, Opt. Express 25, 2709 (2017).
  16. H. Cao, T. Čižmár, S. Turtaev, T. Tyc, and S. Rotter, Controlling light propagation in multimode fibers for imaging, spectroscopy, and beyond, Adv. Opt. Photonics 15, 524 (2023).
  17. S. M. Popoff, G. Lerosey, R. Carminati, M. Fink, A. C. Boccara, and S. Gigan, Measuring the transmission matrix in optics: An approach to the study and control of light propagation in disordered media, Phys. Rev. Lett. 104, 100601 (2010).
  18. P. Caramazza, O. Moran, R. Murray-Smith, and D. Faccio, Transmission of natural scene images through a multimode fibre, Nat. Commun. 10, 2029 (2019).
  19. W. Fan, Z. Chen, V. V. Yakovlev, and J. Pu, High-fidelity image reconstruction through multimode fiber via polarization-enhanced parametric speckle imaging, Laser Photonics Rev. 15, 2000376 (2021).
  20. C. Zhu, E. A. Chan, Y. Wang, W. Peng, R. Guo, B. Zhang, C. Soci, and Y. Chong, Image reconstruction through a multimode fiber with a simple neural network architecture, Sci. Rep. 11, 896 (2021).
  21. N. Borhani, E. Kakkava, C. Moser, and D. Psaltis, Learning to see through multimode fibers, Optica 5, 960 (2018).
  22. A. Abdulaziz, S. P. Mekhail, Y. Altmann, M. J. Padgett, and S. McLaughlin, Robust real-time imaging through flexible multimode fibers, Sci. Rep. 13, 11371 (2023).
  23. G. Trad and S. Burger, Artificial intelligence-assisted beam distribution imaging using a single multimode fiber at CERN, in Proceedings of the 13th International Particle Accelerator Conference, IPAC-2022, Bangkok, Thailand (JACoW, Geneva, Switzerland, 2022), p. 339.
  24. Q. Xu, A. Hill, H. Zhang, C. Welsch, F. Roncarolo, and G. Trad, Estimation of beam transverse parameters through a multimode fiber using deep learning, in Proceedings of the 13th International Beam Instrumentation Conference, IBIC-2024, Beijing (JACoW, Geneva, Switzerland, 2024), p. 170.
  25. J. Pauwels, G. Van der Sande, and G. Verschaffelt, Space division multiplexing in standard multi-mode optical fibers based on speckle pattern classification, Sci. Rep. 9, 17597 (2019).
  26. H. Zhang, R. Fiorito, A. Shkvarunets, R. Kishek, and C. Welsch, Beam halo imaging with a digital optical mask, Phys. Rev. ST Accel. Beams 15, 072803 (2012).
  27. B. Riddick, E. Montgomery, R. Fiorito, H. Zhang, A. Shkvarunets, Z. Pan, and S. Khan, Photocathode quantum efficiency mapping at high resolution using a digital micromirror device, Phys. Rev. ST Accel. Beams 16, 062802 (2013).
  28. S. Li, S. Alverson, D. Bohler, A. Egger, A. Fry, S. Gilevich, Z. Huang, A. Miahnahri, D. Ratner, J. Robinson et al., Ultraviolet laser transverse profile shaping for improving x-ray free electron laser performance, Phys. Rev. Accel. Beams 20, 080704 (2017).
  29. S. Li, F. Cropp, K. Kabra, T. Lane, G. Wetzstein, P. Musumeci, and D. Ratner, Electron ghost imaging, Phys. Rev. Lett. 121, 114801 (2018).
  30. F. Karstens, GigE multi-camera synchronization, White Paper, Basler AG, 2016.
  31. P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, Image-to-image translation with conditional adversarial networks, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, New York, 2017), pp. 1125–1134.
  32. A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, and A. A. Bharath, Generative adversarial networks: An overview, IEEE Signal Process. Mag. 35, 53 (2018).
  33. K. Okamoto, Fundamentals of Optical Waveguides, 3rd ed. (Academic Press, New York, 2022).
  34. A. Efimov, Coherence and speckle contrast at the output of a stationary multimode optical fiber, Opt. Lett. 43, 4767 (2018).
  35. F. Ren, Y. Xie, X. Pi, and X. Wang, Bridge the gap between simulated and real-world data in optical fiber mode decomposition for accuracy improvement: A deep learning-based co-learning framework with visual similarity-based matching, Expert Syst. Appl. 256, 124937 (2024).
  36. B. Yan, J. Zhang, S. Mi, M. Wang, C. Wang, G. Fan, and P. Zhang, MT-SCUNet: A hybrid neural network for enhanced mode decomposition in optical fibers, Opt. Fiber Technol. 93, 104196 (2025).
  37. S. Farahi, D. Ziegler, I. N. Papadopoulos, D. Psaltis, and C. Moser, Dynamic bending compensation while focusing through a multimode fiber, Opt. Express 21, 22504 (2013).
  38. R. Zhu, J. Luo, X. Zhou, H. Feng, and F. Xu, Anti-perturbation multimode fiber imaging based on the active measurement of the fiber configuration, ACS Photonics 10, 3476 (2023).
  39. G. S. D. Gordon, M. Gataric, A. G. C. P. Ramos, R. Mouthaan, C. Williams, J. Yoon, T. D. Wilkinson, and S. E. Bohndiek, Characterizing optical fiber transmission matrices using metasurface reflector stacks for lensless imaging without distal access, Phys. Rev. X 9, 041050 (2019).
  40. S. Resisi, S. M. Popoff, and Y. Bromberg, Image transmission through a dynamically perturbed multimode fiber by deep learning, Laser Photonics Rev. 15, 2000553 (2021).
  41. A. Dosovitskiy et al., An image is worth 16x16 words: Transformers for image recognition at scale, arXiv:2010.11929.

Outline

Information

Phys. Rev. Accel. Beams 29, 032801– Published 9 March, 2026
  • Received 22 August 2025
  • Accepted 11 February 2026
Crossmark - Check for updates button
Creative Commons Logo

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

Published by the American Physical Society

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation