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001     491187
005     20250720040757.0
024 7 _ |a 10.21468/SciPostPhysCore.5.4.049
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024 7 _ |a arXiv:2112.05043
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024 7 _ |a 10.3204/PUBDB-2023-00044
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041 _ _ |a English
088 _ _ |a arXiv:2112.05043
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088 _ _ |a DESY-21-211
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100 1 _ |a Rentería-Estrada, David F.
|0 D.F.RenteriaEstrada.1
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245 _ _ |a Reconstructing partonic kinematics at colliders with Machine Learning
260 _ _ |a Amsterdam
|c 2022
|b SciPost Foundation
336 7 _ |a article
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500 _ _ |a SciPost Phys. Core 5, 049 (2022). 37 pages + appendices, 16 figures, 7 tables
520 _ _ |a 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.
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650 _ 7 |a p p: scattering
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650 _ 7 |a quantum electrodynamics: correction
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650 _ 7 |a hadron: structure
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650 _ 7 |a photon: direct production
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650 _ 7 |a hadron: production
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650 _ 7 |a higher-order: 1
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650 _ 7 |a higher-order: 0
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650 _ 7 |a kinematics
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650 _ 7 |a correlation
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650 _ 7 |a Monte Carlo
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650 _ 7 |a parton: scattering
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650 _ 7 |a quantum chromodynamics: correction
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650 _ 7 |a data analysis method
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650 _ 7 |a factorization: collinear
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650 _ 7 |a parton: distribution function
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650 _ 7 |a transverse momentum: momentum spectrum
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650 _ 7 |a momentum
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700 1 _ |a Hernández-Pinto, Roger J.
|0 R.J.Hernandez.Pinto.1
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700 1 _ |a Sborlini, German F. R.
|0 P:(DE-H253)PIP1094299
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700 1 _ |a Zurita, Pia
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773 _ _ |a 10.21468/SciPostPhysCore.5.4.049
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787 0 _ |a Rentería-Estrada, David F. et.al.
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|r DESY-21-211 ; arXiv:2112.05043
|t Reconstructing partonic kinematics at colliders with Machine Learning
856 4 _ |y OpenAccess
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