Deeply Learning Deep Inelastic Scattering Kinematics

TL;DR

This paper demonstrates that deep learning techniques can significantly improve the reconstruction of kinematic variables in deep inelastic scattering, surpassing classical methods by leveraging correlations in simulated electron-proton collision data.

Contribution

The study introduces a deep learning approach that outperforms classical reconstruction methods for DIS kinematics using simulated ZEUS data.

Findings

Neural networks surpass classical methods in reconstructing $Q^2$ and $x$.

Deep learning effectively utilizes correlations in detector measurements.

Approach is scalable with large simulated datasets.

Abstract

We study the use of deep learning techniques to reconstruct the kinematics of the neutral current deep inelastic scattering (DIS) process in electron-proton collisions. In particular, we use simulated data from the ZEUS experiment at the HERA accelerator facility, and train deep neural networks to reconstruct the kinematic variables $Q^2$ and $x$. Our approach is based on the information used in the classical construction methods, the measurements of the scattered lepton, and the hadronic final state in the detector, but is enhanced through correlations and patterns revealed with the simulated data sets. We show that, with the appropriate selection of a training set, the neural networks sufficiently surpass all classical reconstruction methods on most of the kinematic range considered. Rapid access to large samples of simulated data and the ability of neural networks to effectively extract information from large data sets, both suggest that deep learning techniques to reconstruct DIS kinematics can serve as a rigorous method to combine and outperform the classical reconstruction methods.