Author ORCID Identifier

Date Available


Year of Publication


Degree Name

Doctor of Philosophy (PhD)

Document Type

Doctoral Dissertation


Arts and Sciences


Physics and Astronomy

First Advisor

Dr. Michael Eides

Second Advisor

Dr. Sergei Gleyzer


The calculation of particle interaction squared amplitudes is a key step in the calculation of cross sections in high-energy physics. These complex calculations are currently performed using domain-specific symbolic algebra tools, where the computational time escalates rapidly with an increase in the number of loops and final state particles. This dissertation introduces an innovative approach: employing a transformer-based sequence-to-sequence model capable of accurately predicting squared amplitudes of Standard Model processes up to one-loop order when trained on symbolic sequence pairs. The primary objective of this work is to significantly reduce the computational time and, more importantly, develop a model that efficiently scales with the complexity of the processes. To the best of our knowledge, this model is the first that encapsulates a wide range of symbolic squared amplitude calculations and, therefore, represents a potentially significant advance in using symbolic machine learning techniques for practical scientific computations.

Digital Object Identifier (DOI)