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Workshop: NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences

Symbolic Machine Learning for High Energy Physics Calculations

Abdulhakim Alnuqaydan · Sergei Gleyzer · Harrison Prosper · Eric Reinhardt · Francois Charton · Neeraj Anand


The calculation of cross sections is of paramount importance in high-energy physics. Among other steps, this process involves squaring the particle interaction amplitudes, which can be very computationally expensive. These lengthy calculations are currently done using domain-specific symbolic algebra tools. We demonstrate that a transformer model, when trained on symbolic sequence pairs, can predict correctly the squared amplitudes of the Standard Model processes, namely QED, QCD and EW with an accuracy of 98\%, 97\% and 95\% , respectively, at a speed that is up to two orders of magnitude faster than current symbolic computation frameworks. We briefly note some limitations of the model and suggest possible future directions for this work.

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