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Machine-learning in Secondary Emission Monitor (SEM) optimization

Project Description

Secondary Emission Monitors (SEMs) are used at accelerator facilities around the world to characterize charged particle beams. You will work on the optimization of monitor design, implementation, operation and in particular image analysis.

Your work will include yield studies in simulation and experiments, aiming at improving the absolute calibration, as well as the calibration stability of SEM Foils used in the CERN Super Proton Synchrotron slow extraction lines.

The aim of the project will be to develop new strategies for online calibration, compare SEM measurements with Optical Transition Radiation (OTR)-based monitors and Cherenkov detectors, improve the overall monitor design, including optimization of the electrodes/grids to enhance secondary emission, and in particular enhance data analysis through the application of machine learning techniques.

This interdisciplinary project will be carried out within the pan-European QUASAR Group that is recognized internationally as a leader in beam instrumentation R&D. You will benefit from the comprehensive training program in accelerator science and technology offered at the Cockcroft Institute, complemented by data science training within the centre for doctoral training LIV.DAT.

Subject to funding approval, this project will be carried out over 48 months. You will spend years 1 and 4 in the UK, and be based at CERN during years 2 and 3. Whilst in the UK, a standard RKUK PhD stipend will be paid, during the time at CERN, the usual CERN doctoral student allowance will be paid. A mandatory 6-months industry placement forms part of the project.

To apply for this opportunity, please visit:

Funding Notes

This project is supported by the STFC.

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