Issue |
EPJ Web Conf.
Volume 245, 2020
24th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
|
|
---|---|---|
Article Number | 06023 | |
Number of page(s) | 7 | |
Section | 6 - Physics Analysis | |
DOI | https://doi.org/10.1051/epjconf/202024506023 | |
Published online | 16 November 2020 |
https://doi.org/10.1051/epjconf/202024506023
Lepton identification in Belle II using observables from the electromagnetic calorimeter and precision trackers
The University of Melbourne, VIC, Australia
* e-mail: marco.milesi@unimelb.edu.au
** e-mail: justin.tan@student.unimelb.edu.au
*** e-mail: phillip.urquijo@unimelb.edu.au
Published online: 16 November 2020
We present a major overhaul to lepton identification for the Belle II experiment, based on a novel multi-variate classification algorithm. Boosted decision trees are trained combining measurements from the electromagnetic calorimeter (ECL) and the tracking system. The chosen observables are sensitive to the different physics that governs interactions of hadrons, electrons and muons with the calorimeter crystals. Dedicated classifiers are used in various detector regions and lepton momentum ranges. The tree output is eventually combined with classifiers that rely upon independent measurements from other sub-detectors. Using simulation, the performance of the new algorithm is compared against the method used for analysis of the 2018 Belle II data, namely a likelihood discriminator based on the ratio of energy measured in the ECL over the momentum measured by the trackers. In the low momentum region, we largely improve the lepton-pion separation power, decreasing misidentification probability by a factor of 10 for electrons, and 2 for muons at fixed identification efficiency.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.