TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhysCore.5.4.049
TI  - Reconstructing partonic kinematics at colliders with machine learning
PY  - 2022/10/31
UR  - https://scipost.org/SciPostPhysCore.5.4.049
JF  - SciPost Physics Core
JA  - SciPost Phys. Core
VL  - 5
IS  - 4
SP  - 049
A1  - Rentería, David
AU  - Hernández-Pinto, Roger J.
AU  - Sborlini, German
AU  - Zurita, Pia
AB  - 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. In proton-proton collisions, this represents a challenging task since extracting such information from experimental data is not straightforward. With this in mind, we propose to tackle this problem by studying the production of one hadron and a direct photon in proton-proton collisions, including up to Next-to-Leading Order Quantum Chromodynamics and Leading-Order Quantum Electrodynamics corrections. Using 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.
ER  -

@Article{10.21468/SciPostPhysCore.5.4.049,
	title={{Reconstructing partonic kinematics at colliders with machine learning}},
	author={David F. Rentería Estrada and  Roger J. Hernández-Pinto and  German F. R. Sborlini and Pia Zurita},
	journal={SciPost Phys. Core},
	volume={5},
	pages={049},
	year={2022},
	publisher={SciPost},
	doi={10.21468/SciPostPhysCore.5.4.049},
	url={https://scipost.org/10.21468/SciPostPhysCore.5.4.049},
}