State-space models for squared amplitude calculation in high-energy physics


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.

Task ideas and expected results


Significant experience with machine learning models in Python (preferably using pytorch).

Difficulty Level



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Corresponding Project

Participating Organizations