Investigation of Machine Learning for Jet Momentum Reconstruction in Heavy Ion Collisions - Undergraduate Honors Thesis

Aug 2, 2026

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Undergraduate honors thesis investigating machine-learning approaches for reconstructing the transverse momentum of jets produced in heavy-ion collisions. The work evaluates a methodology based on simulated events and compares learning models against established reconstruction strategies, emphasizing resolution improvements and performance across representative jet momentum distributions. The study reviews momentum/angular observables and key physics context such as partons, quark-gluon plasma, jets, and machine-learning workflows, then documents the full implementation and results analysis using evaluation metrics and regression studies.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages49

Reading Time2 h 3 min

AccessFree to download

Introduction

Background

Momentum and Angular Measures

Partons

Quark-Gluon Plasma (QGP)

Jets

Machine Learning

ROOT and Data Storage

Implementation

Methodology

PYTHIA

FastJet

Thermal Model

Preparation for Machine Learning

Area Correction Method

Machine Learning with scikit-learn

Results

Evaluation Metrics

Baseline Model

Evaluation of Distributions and Features

Linear Regression Analysis

Discussion

Final Thoughts

Conclusion

Looking Forward

Acknowledgements

Appendix

Baseline Model: Additional Figures and Tables

Project GitHub Link

Investigation of Machine Learning for Jet Momentum Reconstruction in Heavy Ion Collisions Jordan D. Lang Department of Physics, University of Colorado Boulder Undergraduate Honors Thesis Defended March 22, 2023 Committee Members Jamie Nagle Thesis Advisor, Department of Physics John Cumalat Honors Council Representative, Department of Physics Divya Vernerey Department of Mathematics Contents 1 Introduction 5 2 Background 6 2.1 Momentum and Angular Measures ....................... 6 2.2 Partons ...................................... 7 2.3 Quark-Gluon Plasma (QGP) ........................... 11 2.4 Jets ........................................ 12 2.5 Machine Learning ................................. 14 2.6 ROOT and Data Storage ............................. 18 3 Implementation 19 3.1 Methodology ................................... 19 3.2 PYTHIA ...................................... 20 3.3 FastJet ....................................... 22 3.4 Thermal Model .................................. 23 3.5 Preparation for Machine Learning ........................ 24 3.6 Area Correction Method ............................. 25 3.7 Machine Learning with scikit-learn ....................... 26 4 Results 28 4.1 Evaluation Metrics ................................ 28 4.2 Baseline Model .................................. 28 4.3 Evaluation of Distributions and Features .................... 30 4.4 Linear Regression Analysis ............................ 33 5 Discussion 38 5.1 Final Thoughts .................................. 38 5.2 Conclusion ..................................... 38 5.3 Looking Forward ................................. 39 5.4 Acknowledgements ................................ 40 6 Appendix 41 6.1 Baseline Model: Additional Figures and Tables ................ 41 6.2 Project GitHub Link ............................... 47 Dedication To the people in my life who showed me you’re never too old to learn something new. Barbara Lang Daniel Lang Lois Lang Ted Alderson Vanessa Zacarias Acknowledgements I could not have made it this far without the support of so very many people. I wish I could include them all here, but I think that would be another thesis in itself. Instead, I wish to recognize and thank the mentors who guided me along on my journey into physics: Professors Jamie Nagle and Dennis Perepelitsa, who brought me into their lab group and provided me with this amazing opportunity to learn about heavy ion physics. Through them, I realized my passion for the field of nuclear and particle physics. I feel incredibly lucky to have had this undergraduate research experience, and to have had both of them as mentors. Professor Shuo Sun, who gave me my first undergraduate research experience and introduced me to quantum optics and photonics. I learned so much from him about hands-on experimentation and starting a research lab, and it has been wonderful to see his lab grow. Steve Swingle from City College of San Francisco, who taught all of my introductory physics classes. He was my first real mentor in physics, and his passion for teaching inspired me to pursue physics as a career. Finally, I must thank Dr. Maria Falbo who taught my high school physics class. She saw my affinity for physics well before I did, and I still remember her telling me to consider it instead of design. It took me a few years, but I finally got the message. 1 Introduction This thesis covers my undergraduate work in the Heavy Ions Group at the University of Colorado Boulder, investigating the application of machine learning methods to the reconstruction of transverse momentum of jets in heavy ion collisions. This project largely follows from the paper Machine-learning-based jet momentum reconstruction in heavy-ion collisions from Rudiger Haake and Constantin Loizides, in which they simulate heavy ion collisions with a toy model and use it to train three mac

FAQs

What problem does the thesis address in heavy-ion physics?

The thesis targets reconstructing the transverse momentum of jets in heavy-ion collisions using machine-learning methods, aiming to assess the validity of prior reported improvements.

Which components are reviewed as background material?

It reviews core concepts including momentum and angular measures, partons, quark-gluon plasma, jets, and an overview of machine learning, along with ROOT and data storage.

How are results evaluated in the thesis?

Results are assessed using evaluation metrics, comparisons to baseline and linear regression approaches, and analysis of distributions and features for simulated jet momentum cases.

Aug 2, 2026

Upload by Aurora