Machine learning representation of the F2 structure function over all charted Q2 and x range
Year: 2021
Type: article
Abstract: Structure function data provide insight into the nucleon quark distribution. They are relatively straightforward to extract from the world's vast, and growing, amount of inclusive leptoproduction data. In turn, structure functions can be used to model the physical processes needed for planning and optimizing future experiments. In this paper a machine learning algorithm capable of predicting, using a unique set of parameters, the ${F}_{2}$ structure function, for four-momentum transfer $0.055\ensuremath{\le}{Q}^{2}\ensuremath{\le}800.0\phantom{\rule{4pt}{0ex}}{\mathrm{GeV}}^{2}$ and for Bjorken $x$ from $2.8\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}5}$ to the pion threshold, is presented. The model was trained and reproduces the hydrogen and the deuterium data at a level comparable with the average uncertainty of the experimental data. Extending the model to heavier nuclei or expanding the kinematic range is straightforward. The model is at least ten times faster than existing grid-based structure functions parametrizations that rely on interpolation and a hundred times faster than models requiring convolutions, making it an ideal candidate for event generators and systematic studies.
Source: Physical Review C
Authors: S. Brown, G. Niculescu, I. Niculescu
Institution: Virginia Tech
Language: Latvian
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Funder: National Science Foundation
OpenAlex ID: W4200317776