JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@inproceedings{trad:ipac2022-mopopt041,
author = {G. Trad and S. Burger},
title = {{Artificial Intelligence-Assisted Beam Distribution Imaging Using a Single Multimode Fiber at CERN}},
booktitle = {Proc. IPAC'22},
% booktitle = {Proc. 13th International Particle Accelerator Conference (IPAC'22)},
pages = {339--342},
eid = {MOPOPT041},
language = {english},
keywords = {network, experiment, simulation, coupling, detector},
venue = {Bangkok, Thailand},
series = {International Particle Accelerator Conference},
number = {13},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {07},
year = {2022},
issn = {2673-5490},
isbn = {978-3-95450-227-1},
doi = {10.18429/JACoW-IPAC2022-MOPOPT041},
url = {https://jacow.org/ipac2022/papers/mopopt041.pdf},
abstract = {{In the framework of developing radiation tolerant imaging detectors for transverse beam diagnostics, the use of machine learning powered imaging using optical fibers is explored for the first time at CERN. This paper presents the pioneering work of using neural networks to reconstruct the scintillating screen beam image transported from a harsh radioactive environment over a single, large-core, multimode, optical fiber. Profiting from generative modeling used in image-to-image translation, conditional adversarial networks have been trained to translate the output plane of the fiber, imaged on a CMOS camera, into the beam image imprinted on the scintillating screen. Theoretical aspects, covering the development of the dataset via geometric optics simulations, modeling the image propagation in a simplified model of an optical fiber, and its use for training the network are discussed. Finally, the experimental setups, both in the laboratory and at the CLEAR facility at CERN, used to validate the technique and evaluate its potential are highlighted.}},
}