Speaker
Prof.
simonetta liuti
(university of virginia)
Description
We will present a method to extract parton distribution functions from hard scattering processes based on an alternative type of neural networks, the Self-Organizing Maps (SOMs). Quantitative results including a detailed treatment of uncertainties will be presented within a Next to Leading Order analysis of both unpolarized and polarized inclusive deep inelastic scattering data. With a fully working procedure in hand, we are capable to extend our analysis to the Generalized Parton Distribution (GPD) case, thus exploiting the “classification” and “visualization” properties of the SOMs.
Work supported by US D.O.E. grant DE-FG02-01ER41200. We thank for computer time the University of Virginia Alliance for Computational Science and Engineering, and the HPC group at Jefferson Lab.
Primary author
Prof.
simonetta liuti
(university of virginia)
