Reconstruction of Transverse Beam Distribution using Machine Learning

The beam transverse distribution in CERN's high-radiation environment is measured by imaging the light generated by the particle beam hitting a scintillating screen, using cameras produced in-house based on radiation hard tubes. Due to the cessation of radiation hard tube production worldwide, CERN is investigating the transport of the beam image to low-radiation areas using radiation tolerant optical fibers coupled to normal CMOS cameras. In this framework, pioneering work to reconstruct the beam's transverse distribution using a single large-core multimode optical fiber began in 2020. It takes advantage of advances in generative modeling using deep learning methods, such as convolutional neural networks, and attempts to apply them to beam diagnostics. In this PhD project, the student will refine simulated data sets with more realistic optical modeling of the imaging fiber, taking into account environmental factors such as temperature and vibration effects. The student will then optimize the fiber's parameters, such as diameter and numerical aperture, and perform a market survey for available radiation-resistant fibers. In a next step, the student will screen available networks for image translation, in particular the convolutional U-Net, widely used for biomedical image segmentation and the already used generative adversarial networks. The student will then develop a machine learning-based model using simulated datasets and evaluate its performance. On the basis of these results, the student will develop an experimental setup to validate the simulation results and carry out a measurement campaign at CERN's CHARM irradiation facility to verify and study the accumulated dose related degradation effect to be included in the model. The student will have access to the Cockcroft Institute's comprehensive postgraduate training in accelerator science, as well as to LIV.INNO's structured training. The student will spend years 1 and 4 in the UK, and be based at CERN during years 2 and 3.