Reconstructing partonic kinematics at colliders with Machine Learning

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.

28 pages + appendices, 16 figures, 7 tables

References in corpus (17)

• LHAPDF6: parton density access in the LHC precision era
• Kernel methods in machine learning
• The Path to Proton Structure at One-Percent Accuracy
• How bright is the proton? A precise determination of the photon parton distribution function
• Dihadron azimuthal correlations in Au+Au collisions at sqrt(s_NN)=200 GeV
• Global Analysis of Fragmentation Functions for Protons and Charged Hadrons
• Parton-to-Pion Fragmentation Reloaded
• The Photon Content of the Proton
• Science Requirements and Detector Concepts for the Electron-Ion Collider: EIC Yellow Report
• From loops to trees by-passing Feynman's theorem
• Four-dimensional unsubtraction with massive particles
• May the four be with you: Novel IR-subtraction methods to tackle NNLO calculations
• Optimising simulations for diphoton production at hadron colliders using amplitude neural networks
• Pion Fragmentation Functions at High Energy Colliders
• From multileg loops to trees (by-passing Feynman's Tree Theorem)
• Reconstructing the Kinematics of Deep Inelastic Scattering with Deep Learning
• Reframing Jet Physics with New Computational Methods

Cited by in corpus (2)

• Constraining fragmentation functions through hadron-photon production at higher-orders
• Phenomenological extraction of fragmentation functions in a $p\bar{p}$ environment