Investigation of Machine Learning for Jet Momentum Reconstruction in Heavy Ion Collisions

Undergraduate Honors Thesis

Citations

Citeable URL: https://scholar.colorado.edu/concern/undergraduate_honors_theses/2n49t3101

Abstract

This thesis investigates 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 Haake and Loizides, in which they simulate heavy ion collisions and train three machine learning algorithms that reconstruct jet momentum. My project investigates this methodology and the validity of these results, explores the viability of linear regression as a transparent alternative to machine learning, and considers whether machine learning is an appropriate tool for jet momentum reconstruction. We provide an overview of useful background information including: quark- gluon plasma, jets, and machine learning. Following is a detailed review of the methodology, covering the software used and specific implementation. Results for three simulated jet momentum distributions are presented with further analysis of a baseline model using linear regression. Finally, we reflect on the implications of this project and suggests avenues for future investigations.

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