Kinematic fitting of Neutral current events in deep inelastic ep collisions

Abstract

In this paper we present a technique to reconstruct the scaling variables defining $ep$ deep inelastic scattering by performing a kinematic fit. This reconstruction technique makes use of the full potential of the data collected. It is based on Bayes Theorem and involves the use of informative priors. The kinematic fit method has been tested for the neutral current ep simulated sample at a center of mass energy of 318 GeV with $Q^2$ > 400 GeV${}^2$. In addition to the scaling variables, this method is able to estimate the energy of possible initial state radiation (E${}_{\gamma }$ ) which otherwise goes undetected. A better resolution than standard electron and double angle techniques in the reconstruction of scaling variables is achieved using a kinematic fit.

17 pages, 12 figures, revised version of paper

References in corpus (2)

• Reconstructing the Kinematics of Deep Inelastic Scattering with Deep Learning
• Deeply Learning Deep Inelastic Scattering Kinematics