@article{Rentería Estrada_Hernandez-Pinto_Sborlini_Zurita_2024, title={Precision studies for the partonic kinematics calculation through Machine Learning}, volume={4}, url={https://rmf.smf.mx/ojs/index.php/rmf-s/article/view/7126}, DOI={10.31349/SuplRevMexFis.4.021134}, abstractNote={<p>High Energy collider experiments are moving to the highest precision frontier quickly. The predictions of observables are based on the factorization formula which helps to connect small to large distances. These predictions can be contrasted with experimental measurements and the success of this phenomenological approach is based on the correct description of nature. The application of the method to proton-proton colliders brings new challenges due to the proton structure and the detectors efficiency on reconstructing hadrons. Furthermore, since the non-perturbative distribution functions takes an important role to describe the experimental distributions, the presence of them makes the information of the partons diluted. At Leading Order (LO) in perturbative calculations, the momentum fractions involved in hard scattering processes are known exactly in terms of kinematical variables of initial and final states hadrons. However, at Next-to-Leading Order (NLO) and beyond, a closed analytical formula is not available. Furthermore, from the pure theoretical calculation, the exact definition of the momentum fraction is very challenging. In this work, we report a methodology based on Machine Learning techniques for the extraction of momentum fractions for $p+p\to\pi^++\gamma$ using a Monte Carlo simulation including quantum corrections up to Next-to-Leading Order in Quantum Chromodynamics and Leading Order in Quantum Electrodymics. Our findings point towards a methodology to find the fundamental properties of the internal structure of hadrons because the reconstructed momentum fractions deeply relate our perturbative models with experimental measurements.</p>}, number={2}, journal={Suplemento de la Revista Mexicana de Física}, author={Rentería Estrada, David Francisco and Hernandez-Pinto, Roger J. and Sborlini, German F. R. and Zurita, Pia}, year={2024}, month={Jan.}, pages={021134 1–6} }