| |
| author |
Brendan T. Mullany
| | title |
Using Boosted Decision Trees to Select High Quality Measurements in the Mu2e Experiment at Fermilab
| | abstract |
This thesis presents the implementation and evaluation of a Boosted Decision Tree (BDT)
model to improve the selection of high-quality track measurements in the Mu2e experiment
at Fermilab. The Mu2e experiment is a high-energy physics experiments seeking to observe
a rare theoretical physics process known as Charged Lepton Flavor Violation. A significant
challenge faced by the Mu2e experiment are so-called background events, which are events
whose data mimics that of the rare physics process the experiment seeks to observe. Without
a mechanism to reduce background, it would be impossible to know whether Charged Lepton
Flavor Violation occurred or not. To this end, high-quality track measurements must be
distinguished from low-quality track measurements. A track can be conceived of as the
reconstructed path of a particle that traveled through the Mu2e detector. In addition to
other data, data about such tracks is stored using a C++-based framework, specific to the
domain of high-energy physics, known as ROOT. A boosted decision tree model was trained
using ROOT's Toolkit For Multivariate Analysis by leveraging variables ancillary to track
quality. In evaluation, the BDT achieves a ROC-AUC of 0.927 in discriminating good-quality
tracks from poor-quality tracks. Such a score is indicative of both strong discrimination
and strong generalization. Subsequently, it is shown that applying a BDT-based quality
cut to the distribution of particle momenta significantly enhances the signal-to-background
distinction for signal electrons, paving the way for improved sensitivity to Charged Lepton
Flavor Violation.
| | school |
The College of Liberal Arts, Drew University
| | degree |
B.S. (2025)
|
| advisor |
Kamal Benslama
|
| committee |
Barry Burd
Erik Anderson
|
| full text | BMullany.pdf - requires Drew uLogin |
| |
