One of the most important physical quantities in particle physics is the cross section, or a probability that a particular process takes place in the interaction of elementary particles. Its measure provides a testable link between theory and experiment. It is obtained theoretically mainly by calculating the squared amplitude. This project aims to build a foundation model for squared amplitude calculation. This project is closely related to the projects “Physics-Informed Encoding and Decoding for Squared Amplitude Calculation” and “Physics-Informed Models for Squared Amplitude Calculation”. Complete foundation model solutions should build on the ideas from those proposals incorporating physics-informed representations and architectures.
Total project length: 175/350 hours.
Significant experience with machine learning models in Python (preferably using pytorch). Previous work in physics informed machine learning and/or graph models is preferred.
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Please DO NOT contact mentors directly by email. Questions should instead be directed to ml4-sci@cern.ch which is forwarded to mentors. To submit your proposal, CV, and test task solutions, please use this Google form.
