SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learning

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 QCD and QED 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.

References in corpus (6)

• CPTR: Full Transformer Network for Image Captioning
• A Living Review of Machine Learning for Particle Physics
• SymbolicGPT: A Generative Transformer Model for Symbolic Regression
• Deep Symbolic Regression for Recurrent Sequences
• BEHRT: Transformer for Electronic Health Records
• MARTY -- Modern ARtificial Theoretical phYsicist: A C++ framework automating symbolic calculations Beyond the Standard Model

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