Reconstructing partonic kinematics at colliders with Machine Learning

TL;DR

This paper demonstrates how Machine Learning can accurately reconstruct partonic kinematics in high-energy collider events, enhancing understanding of hadron structure and improving calculation precision.

Contribution

It introduces a novel ML-based method to determine parton momentum fractions from collider data, integrating NLO QCD and LO QED corrections.

Findings

ML algorithms accurately predict parton momentum fractions

Results align with previous studies, confirming validity

Shows ML's potential for high-precision collider analysis

Abstract

In the context of high-energy physics, a reliable description of the parton-level kinematics plays a crucial role for understanding the internal structure of hadrons and improving the precision of the calculations. Here, we study the production of one hadron and a direct photon, including up to Next-to-Leading Order Quantum Chromodynamics and Leading-Order Quantum Electrodynamics corrections. Using a code based on Monte-Carlo integration, we simulate the collisions and analyze the events to determine the correlations among measurable and partonic quantities. Then, we use these results to feed three different Machine Learning algorithms that allow us to find the momentum fractions of the partons involved in the process, in terms of suitable combinations of the final state momenta. Our results are compatible with previous findings and suggest a powerful application of Machine-Learning to model high-energy collisions at the partonic-level with high-precision.