- 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

Reconstructing time-of-flight detector values of angular streaking using machine learning

David Meier, Jens Viefhaus, and Gregor Hartmann

Wolfram Helml

Thorsten Otto

Bernhard Sick

PDF

Phys. Rev. Accel. Beams 28, 074601 – Published 21 July, 2025

DOI: https://doi.org/10.1103/csvm-858f

Abstract

Angular streaking experiments enable for experimentation in the attosecond regions. However, the deployed time-of-flight (TOF) detectors are susceptible to noise and failure. These shortcomings make the outputs of the TOF detectors hard to understand for humans and further processing, such as, for example, the extraction of beam properties. In this article, we present an approach to remove high noise levels and reconstruct up to three failed TOF detectors from an arrangement of 16 TOF detectors. Due to its fast evaluation time, the presented method is applicable online during a running experiment. It is trained with simulation data, and we show the results of denoising and reconstruction of our method on real-world experiment data.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (28)

  1. W. Decking et al., A MHz-repetition-rate hard x-ray free-electron laser driven by a superconducting linear accelerator, Nat. Photonics 14, 391 (2020).
  2. C. Bostedt, S. Boutet, D. M. Fritz, Z. Huang, H. J. Lee, H. T. Lemke, A. Robert, W. F. Schlotter, J. J. Turner, and G. J. Williams, Linac coherent light source: The first five years, Rev. Mod. Phys. 88, 015007 (2016).
  3. N. Hartmann et al., Attosecond time–energy structure of x-ray free-electron laser pulses, Nat. Photonics 12, 215 (2018).
  4. U. S. Sainadh, H. Xu, X. Wang, A. Atia-Tul-Noor, W. C. Wallace, N. Douguet, A. Bray, I. Ivanov, K. Bartschat, A. Kheifets, R. T. Sang, and I. V. Litvinyuk, Attosecond angular streaking and tunnelling time in atomic hydrogen, Nature (London) 568, 75 (2019).
  5. K. Dingel, T. Otto, L. Marder, L. Funke, A. Held, S. Savio, A. Hans, G. Hartmann, D. Meier, J. Viefhaus, B. Sick, A. Ehresmann, M. Ilchen, and W. Helml, Artificial intelligence for online characterization of ultrashort x-ray free-electron laser pulses, Sci. Rep. 12, 17809 (2022).,
  6. F. Bornemann and T. März, Fast image inpainting based on coherence transport, J. Math. Imaging Vision 28, 259 (2007).
  7. H. Hukkelås, F. Lindseth, and R. Mester, Image inpainting with learnable feature imputation, in Pattern Recognition, edited by Z. Akata, A. Geiger, and T. Sattler (Springer International Publishing, Cham, 2021), pp. 388–403, 10.1007/978-3-030-71278-5_280.
  8. R. Köhler, C. Schuler, B. Schölkopf, and S. Harmeling, Mask-specific inpainting with deep neural networks, in Pattern Recognition, edited by X. Jiang, J. Hornegger, and R. Koch (Springer International Publishing, Cham, 2014), pp. 523–534, 10.1007/978-3-319-11752-2_43.
  9. J. Xie, L. Xu, and E. Chen, Image denoising and inpainting with deep neural networks, in Proceedings of the 25th International Conference on Neural Information Processing Systems (Curran Associates Inc., Red Hook, NY, 2012), Vol. 1, pp. 341–349.
  10. G. Liu, F. A. Reda, K. J. Shih, T.-C. Wang, A. Tao, and B. Catanzaro, Image inpainting for irregular holes using partial convolutions, in Computer Vision–ECCV 2018, edited by V. Ferrari, M. Hebert, C. Sminchisescu, and Y. Weiss (Springer International Publishing, Cham, 2018), pp. 89–105, 10.1007/978-3-030-01252-6_6.
  11. C. Xie, S. Liu, C. Li, M.-M. Cheng, W. Zuo, X. Liu, S. Wen, and E. Ding, Image inpainting with learnable bidirectional attention maps, in Proceedings of the 2019 IEEE / CVF International Conference on Computer Vision (ICCV), Seoul, Korea (IEEE, New York, 2019), pp. 8857–8866, 10.1109/ICCV.2019.00895.
  12. J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu, and T. Huang, Free-form image inpainting with gated convolution, in Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (IEEE, New York, 2019), pp. 4470–4479, 10.1109/ICCV.2019.00457.
  13. Y. Kossale, M. Airaj, and A. Darouichi, Mode collapse in generative adversarial networks: An overview, in Proceedings of the 8th International Conference on Optimization and Applications (ICOA), Genoa, Italy (IEEE, New York, 2022), pp. 1–6, 10.1109/ICOA55659.2022.9934291.
  14. M. Arjovsky, S. Chintala, and L. Bottou, Wasserstein generative adversarial networks, in Proceedings of the 34th International Conference on Machine Learning Research, edited by D. Precup and Y. W. Teh (PMLR, Sydney, Australia, 2017), Vol. 70, pp. 214–223.
  15. M. H. Givkashi, M. Hadipour, A. PariZanganeh, Z. Nabizadeh, N. Karimi, and S. Samavi, Image inpainting using autoencoder and guided selection of predicted pixels, in Proceedings of the 30th International Conference on Electrical Engineering (ICEE), Tehran, Iran (IEEE, New York, 2022), pp. 700–704, 10.1109/ICEE55646.2022.9827427.
  16. S. Schubert, P. Neubert, J. Pöschmann, and P. Protzel, Circular convolutional neural networks for panoramic images and laser data, in Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV) (2019), p. 653, 10.1109/IVS.2019.8813862
  17. O. Ronneberger, P. Fischer, and T. Brox, U-Net: Convolutional networks for biomedical image segmentation, Lect Notes Comput. Sci. 9351, 234 (2015).
  18. A. Lugmayr, M. Danelljan, A. Romero, F. Yu, R. Timofte, and L. Van Gool, Repaint: Inpainting using denoising diffusion probabilistic models, in Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Antalya, Turkey (IEEE, New York, 2022), pp. 11451–11461, 10.1109/CVPR52688.2022.01117.
  19. A. Scheinker and R. Pokharel, Enabling dynamic 3D coherent diffraction imaging via adaptive latent space tuning of generative autoencoders, npj Comput. Mater. 10, 283 (2024).
  20. A. Scheinker and A. Williams, Adaptive conditional latent diffusion maps beam loss to 2D phase space projections, arXiv:2502.18684.
  21. https://github.com/hz-b/tof-reconstruction/blob/main/data_generation.py
  22. C. N. Yang, On the angular distribution in nuclear reactions and coincidence measurements, Phys. Rev. 74, 764 (1948).
  23. M. I. Hosen and M. B. Islam, Masked face inpainting through residual attention UNet, in Proceedings of the Innovations in Intelligent Systems and Applications Conference (ASYU), Antalya, Turkey (IEEE, New York, 2022), pp. 1–5, 10.1109/ASYU56188.2022.9925541.
  24. D. Misra, Mish: A self regularized nonmonotonic activation function, arXiv:1908.08681.
  25. I. Loshchilov and F. Hutter, Decoupled weight decay regularization, arXiv:1711.05101.
  26. J. Lim, Two-Dimensional Signal and Image Processing (Prentice Hall, Englewood Cliffs, NJ, 1990).
  27. https://github.com/hz-b/tof-reconstruction
  28. P. Virtanen et al., scipy 1.0: Fundamental algorithms for scientific computing in python, Nat. Methods 17, 261 (2020).

Outline

Information

Phys. Rev. Accel. Beams 28, 074601– Published 21 July, 2025
  • Received 15 January 2025
  • Accepted 26 June 2025
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