  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.
