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000491187 1001_ $$0D.F.RenteriaEstrada.1$$aRentería-Estrada, David F.$$b0$$eCorresponding author
000491187 245__ $$aReconstructing partonic kinematics at colliders with Machine Learning
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000491187 500__ $$aSciPost Phys. Core 5, 049 (2022). 37 pages + appendices, 16 figures, 7 tables
000491187 520__ $$aIn the context of high-energy physics, a reliable description of the parton-level kinematics plays a crucial role for understanding the internal structure of hadrons and improving the precision of the calculations. Here, we study the production of one hadron and a direct photon, including up to Next-to-Leading Order Quantum Chromodynamics and Leading-Order Quantum Electrodynamics corrections. Using a code based on Monte-Carlo integration, we simulate the collisions and analyze the events to determine the correlations among measurable and partonic quantities. Then, we use these results to feed three different Machine Learning algorithms that allow us to find the momentum fractions of the partons involved in the process, in terms of suitable combinations of the final state momenta. Our results are compatible with previous findings and suggest a powerful application of Machine-Learning to model high-energy collisions at the partonic-level with high-precision.
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000491187 650_7 $$2INSPIRE$$aquantum electrodynamics: correction
000491187 650_7 $$2INSPIRE$$ahadron: structure
000491187 650_7 $$2INSPIRE$$aphoton: direct production
000491187 650_7 $$2INSPIRE$$ahadron: production
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000491187 650_7 $$2INSPIRE$$ahigher-order: 0
000491187 650_7 $$2INSPIRE$$akinematics
000491187 650_7 $$2INSPIRE$$acorrelation
000491187 650_7 $$2INSPIRE$$aMonte Carlo
000491187 650_7 $$2INSPIRE$$aparton: scattering
000491187 650_7 $$2INSPIRE$$aquantum chromodynamics: correction
000491187 650_7 $$2INSPIRE$$ahard scattering
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000491187 650_7 $$2INSPIRE$$adata analysis method
000491187 650_7 $$2INSPIRE$$afactorization: collinear
000491187 650_7 $$2INSPIRE$$aparton: distribution function
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000491187 7001_ $$0R.J.Hernandez.Pinto.1$$aHernández-Pinto, Roger J.$$b1
000491187 7001_ $$0P:(DE-H253)PIP1094299$$aSborlini, German F. R.$$b2
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000491187 773__ $$0PERI:(DE-600)3071450-3$$a10.21468/SciPostPhysCore.5.4.049$$gVol. 5, no. 4, p. 049$$n4$$p049$$tSciPost Physics Core$$v5$$x2666-9366$$y2022
000491187 7870_ $$0PUBDB-2021-05010$$aRentería-Estrada, David F. et.al.$$d2021$$iIsParent$$rDESY-21-211 ; arXiv:2112.05043$$tReconstructing partonic kinematics at colliders with Machine Learning
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