Author ORCID Identifier
Date Available
12-19-2023
Year of Publication
2023
Document Type
Doctoral Dissertation
Degree Name
Doctor of Philosophy (PhD)
College
Arts and Sciences
Department/School/Program
Physics and Astronomy
Advisor
Dr. Michael Eides
Co-Director of Graduate Studies
Dr. Sergei Gleyzer
Abstract
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)
https://doi.org/10.13023/etd.2023.482
Recommended Citation
Alnuqaydan, Abdulhakim, "Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learning" (2023). Theses and Dissertations--Physics and Astronomy. 119.
https://uknowledge.uky.edu/physastron_etds/119
Included in
Artificial Intelligence and Robotics Commons, Elementary Particles and Fields and String Theory Commons
