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. In this project we will explore state-space models (SSMs) to map from amplitudes to squared amplitudes using sequence to sequence representations.
Total project length: 175/350 hours.
Significant experience with machine learning models in Python (preferably using pytorch).
Advanced
Please DO NOT contact mentors directly by email. Instead, please email ml4-sci@cern.ch with Project Title and include your CV and test results. The mentors will then get in touch with you.
