Machine learning representation of the F2 structure function over all charted Q2 and x range

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 F2 structure function, for four-momentum transfer 0.055 Q2 800.0 GeV2 and for Bjorken x from 2.8 x 10-5 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.

Automated summary

Structure function data provide insight into nucleon quark distribution and can be extracted from inclusive leptoproduction data. A machine learning algorithm is presented in this paper to predict F2 structure function for a wide range of parameters, demonstrating speed and accuracy advantages over existing parametrizations. The model has potential for application in event generators and systematic studies.