Please visit Jefferson Lab Event Policies and Guidance before planning your next event: https://www.jlab.org/conference_planning.
29-30 July 2021
Online Only
US/Eastern timezone

Deeply learning deep inelastic scattering kinematics

30 Jul 2021, 13:45
15m
Online Only

Online Only

Speaker

Abdullah Farhat (Old Dominion University)

Description

We studied the use of machine learning to reconstruct deep inelastic scattering (DIS) kinematics. In particular, we trained deep neural networks to reconstruct $x$ and $Q^2$ based on information from the lepton and the hadronic system in ep scattering at the ZEUS experiment at HERA. These models were trained by a careful selection of Monte Carlo events. The results from the neural networks were compared to those of classical reconstruction methods, including the electron method, the Jacquet-Blondel Method, and the double-angle method. The classical methods only consider partial information from an event, each method has its limitations, including a sensitivity to initial and final state QED radiation, a requirement of precise energy measurements, and/or a poor resolution on different kinematic regions. The neural networks trained in our study reconstruct event kinematics based on the information used in the classical methods, but, in addition, are enhanced through correlations and patterns revealed in the simulated data sets from event generators. We show that, with the appropriate selection of a training set, data enhances the neural networks sufficiently to outperform all classical reconstruction methods on most of the kinematic range considered.

Primary authors

Presentation Materials