We present the results of applying a Fast Boosted Decision Tree (FBDT) algorithm to the task of distinguishing muons from pions in K-Long and Muon (KLM) detector of the Belle II experiment. Performance was evaluated over a momentum range of 0.6 < p < 5.0 GeV/c by plotting Receiver Operating Characteristic (ROC) curves for 0.1 GeV/c intervals. The FBDT model was worse than the benchmark likelihood ratio test model for the whole momentum range during testing on Monte Carlo (MC) simulated data. This is seen in the lower Area Under the Curve (AUC) values for the FBDT ROC curves, achieving peak AUC values around 0.82, while the likelihood ratio ROC curves achieve peak AUC values around 0.98. Performance of the FBDT model in muon identification may be improved in the future by adding a pre-processing routine for the MC data and input variables. / Master of Science / An important task of a high-energy physics experiment is taking the input information provided by detectors, such as the distance a particle travels through a detector, the momentum, and energy deposits it makes, and using that information to identify the particle's type. In this study we test a machine learning model that sorts the particles observed into two categories—muons and pions—by comparing the particle's input values to a threshold value at multiple stages, then assigns a final identity to the particle at the last stage. This is compared to a benchmark model that uses the probabilities that these input variables would be seen from a particle of each type to determine which particle type is most likely. The ability of both models to distinguish muons and pions were tested on simulated data from the Belle II detector, and the benchmark model outperformed the machine learning model.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/119076 |
| Date | 23 May 2024 |
| Creators | Benninghoff, Logan Dean |
| Contributors | Physics, Piilonen, Leo E., Takeuchi, Tatsu, Mariani, Camillo |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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