Journal Article

PUBDB-2023-00044

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Reconstructing partonic kinematics at colliders with Machine Learning

Rentería-Estrada, D. F. (Corresponding author) ; Hernández-Pinto, R. J. ; Sborlini, G. F. R.DESY* ; Zurita, P.

2022
SciPost Foundation Amsterdam

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Please use a persistent id in citations: doi:10.21468/SciPostPhysCore.5.4.049  doi:10.3204/PUBDB-2023-00044

Report No.: DESY-21-211; arXiv:2112.05043

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.

Keyword(s): p p: scattering ; quantum electrodynamics: correction ; hadron: structure ; photon: direct production ; hadron: production ; higher-order: 1 ; higher-order: 0 ; kinematics ; correlation ; Monte Carlo ; parton: scattering ; quantum chromodynamics: correction ; hard scattering ; computer ; data analysis method ; factorization: collinear ; parton: distribution function ; transverse momentum: momentum spectrum ; momentum

Note: SciPost Phys. Core 5, 049 (2022). 37 pages + appendices, 16 figures, 7 tables

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Contributing Institute(s):

1. Theorie (ZEU-THEO)
2. Zeuthen Particle PhysicsTheory (Z_ZPPT)

Research Program(s):

1. 611 - Fundamental Particles and Forces (POF4-611) (POF4-611)

Experiment(s):

1. No specific instrument

Appears in the scientific report 2022

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; DOAJ Seal ; Emerging Sources Citation Index ; Web of Science Core Collection

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Linked articles:

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Preprint Rentería-Estrada, D. F. ; Hernández-Pinto, R. J. ; Sborlini, G. F. R. (Corresponding author)DESY* ; Zurita, P.
Reconstructing partonic kinematics at colliders with Machine Learning
[10.3204/PUBDB-2021-05010]     Files  Fulltext by arXiv.org BibTeX | EndNote: XML, Text | RIS

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