Development of FastBDT Classifiers to Suppress Beam Background Clusters and Fake Photons

Sumitted to PubDB: 2023-05-06

Category: Conference papers and plots, Visibility: Public

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Authors Savino Longo, Priyanka Cheema, Chia-Ling Hsu, Bruce Yabsley
Non-Belle II authors Racha Cheaib
Date April 21, 2023
Belle II Number BELLE2-NOTE-PL-2023-003
Abstract We present an overview of the methodology used to develop FastBDT classifiers that suppress beam background clusters and fake photons, with the aim of using these classifiers to improve the signal-background separation power of the residual calorimeter energy E_ECL. The feature selection, hyperparameter tuning, and training of the classifiers is performed using photons from simulated BBbar events. The application of these classifiers to the B∗ → D∗lν decay is shown, with E_ECL distributions before and after classifier cuts also included. These E_ECL distributions also provide a comparison between simulated events and a subset of Belle II data amounting to 25.4 fb−1 of integrated luminosity.

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