Gradient Boosted Decision Tree Application to Muon Identification in the KLM at Belle II

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Virginia Tech


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.



GBDT, binary classifier, particle physics, high-energy physics, SuperKEKB