Reconstructing the Kinematics of Deep Inelastic Scattering with Deep Learning

TL;DR

This paper presents a deep learning approach to reconstruct the kinematics of deep inelastic scattering events, outperforming traditional methods and enhancing the analysis capabilities of future collider experiments.

Contribution

The paper introduces a novel deep neural network method that utilizes full event information and accounts for QED radiation, improving kinematic reconstruction in DIS.

Findings

DNN outperforms traditional methods in resolution and bias

Method applicable to simulated HERA and EIC data

Potential to extend EIC's kinematic reach

Abstract

We introduce a method to reconstruct the kinematics of neutral-current deep inelastic scattering (DIS) using a deep neural network (DNN). Unlike traditional methods, it exploits the full kinematic information of both the scattered electron and the hadronic-final state, and it accounts for QED radiation by identifying events with radiated photons and event-level momentum imbalance. The method is studied with simulated events at HERA and the future Electron-Ion Collider (EIC). We show that the DNN method outperforms all the traditional methods over the full phase space, improving resolution and reducing bias. Our method has the potential to extend the kinematic reach of future experiments at the EIC, and thus their discovery potential in polarized and nuclear DIS.