SYMBA: symbolic computation of squared amplitudes in high energy physics with machine learning. Issue 1 (1st March 2023)

• Record Type: Journal Article
• Authors: Alnuqaydan, Abdulhakim; Gleyzer, Sergei; Prosper, Harrison
• Is Part Of: Machine learning: science and technology. Volume 4:Issue 1(2023)
• Publication Date: 2023-03-01
• Subjects: physics -- high energy physics -- machine learning; 006.31
• DOI: 10.1088/2632-2153/acb2b2
• Languages: English
• ISSNs: 2632-2153

Abstract

Abstract: The cross section is one of the most important physical quantities in high-energy physics and the most time consuming to compute. While machine learning has proven to be highly successful in numerical calculations in high-energy physics, analytical calculations using machine learning are still in their infancy. In this work, we use a sequence-to-sequence model, specifically, a transformer, to compute a key element of the cross section calculation, namely, the squared amplitude of an interaction. We show that a transformer model is able to predict correctly 97.6% and 99% of squared amplitudes of quantum chromodynamics and quantum electrodynamics processes, respectively, at a speed that is up to orders of magnitude faster than current symbolic computation frameworks. We discuss the performance of the current model, its limitations and possible future directions for this work.