Speaker
Description
The T09 beamline of the PS East Experimental Area delivers mixed secondary particle beams for diverse experiments and test beams. This requires the possibility of generating flexible beam conditions. Predicting beam parameters at the experimental area, such as particle composition, spatial profiles, and momentum spread, typically demands time-intensive simulations with tools like BDSIM and FLUKA. This work presents the development and first studies of a surrogate model of the T09 beamline that reproduces key beam characteristics with greatly reduced computational cost, making it a promising candidate for future real-time applications. Trained on a database of detailed BDSIM simulations spanning multiple collimator configurations, the model employs machine-learning-based regression to predict beam transmission, spot size, and phase-space parameters. Preliminary validation against full Monte Carlo simulations shows agreement at a level of \SI{15}{\percent} for the tested configurations, supporting rapid parameter scans and indicating potential for future real-time beam optimisation. This approach is also scalable for other CERN beamlines.
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