Deep Neural Networks for Particle Identification in Simulated Proton-Proton Collisions at LHC and RHIC

Author

Omar Mazhar KhalafFollow

Abstract

Particle identification is an essential part of experimental high-energy physics, which allows the study of the most fundamental constituents of matter. This thesis explores the use of deep neural networks for identifying particles in simulated proton-proton collisions at the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC). The deep neural networks were trained on LHC datasets which have various momentum ranges including regions of high transverse momentum above 3 GeV/c. The key findings of thesis include achieving an accuracy of 99.99%, 98.3%, and 90.14% for 3-5 pt, 5-7 pt and above 7 pt regions respectively for the LHC dataset. Another important finding is that the network generalizes perfectly to the RHIC set (lower center of mass energy) and achieves an accuracy of 99.99% for a regular test set. This thesis highlights the huge potential of neural networks in particle identification, even with relatively simple architectures like multilayer perceptron. Further modifications of the network’s structure can yield even higher accuracies, particularly for the critical high momentum regions.

School

School of Sciences and Engineering

Department

Physics Department

Degree Name

MS in Physics

Graduation Date

Summer 6-15-2025

Submission Date

2-16-2025

First Advisor

Ahmed Hamed

Committee Member 1

Moustafa Youssef

Committee Member 2

Bernd Surrow

Extent

67 p.

Document Type

Master's Thesis

Institutional Review Board (IRB) Approval

Not necessary for this item

Recommended Citation

APA Citation

Khalaf, O. M. (2025).Deep Neural Networks for Particle Identification in Simulated Proton-Proton Collisions at LHC and RHIC [Master's Thesis, the American University in Cairo]. AUC Knowledge Fountain.
https://fount.aucegypt.edu/etds/2491

MLA Citation

Khalaf, Omar Mazhar. Deep Neural Networks for Particle Identification in Simulated Proton-Proton Collisions at LHC and RHIC. 2025. American University in Cairo, Master's Thesis. AUC Knowledge Fountain.
https://fount.aucegypt.edu/etds/2491