The neural network approach to parton fitting
Joan Rojo, Andrea Piccione
Abstract
We introduce the neural network approach to global fits of parton distribution functions. First we review previous work on unbiased parametrizations of deep-inelastic structure functions with faithful estimation of their uncertainties, and then we summarize the current status of neural network parton distribution fits.
Create a lesson
Related papers
Electromagnetic form factors of vector mesons in Einstein-dilaton holographic QCD
Alfonso Ballon-Bayona, Tobias Frederico, Luis A. H. Mamani et al.
An invertible map between 3D Breit-frame mechanical distributions and 2D infinite-momentum-frame mechanical densities in spin-1 hadrons
Kemal Tezgin
Adiabatic hydrodynamization with transverse spatial gradients in boost-invariant plasmas
Uri Sharell, Jasmine Brewer, Weiyao Ke
Line shapes of Ω(2012) production in the Ξ K and Ξπ K decay channels
Natsumi Ikeno, Eulogio Oset
A quantum representation of π fragmentation functions through variational quantum circuits
David F. Rentería-Estrada, Roger J. Hernández-Pinto, Germán Rodrigo et al.
Particle Physics Driven by Quantum Technology - Quantum Simulations and Quantum Sensing
Itay M. Bloch, Marcela Carena, Yifan Chen et al.