Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
|
|
---|---|---|
Article Number | 09035 | |
Number of page(s) | 8 | |
Section | Artificial Intelligence and Machine Learning | |
DOI | https://doi.org/10.1051/epjconf/202429509035 | |
Published online | 06 May 2024 |
https://doi.org/10.1051/epjconf/202429509035
Suppressing Beam Background and Fake Photons at Belle II using Machine Learning
School of Physics, University of Sydney, Australia
* e-mail: pche3675@uni.sydney.edu.au
Published online: 6 May 2024
The Belle II experiment situated at the SuperKEKB energyasymmetric e+ e− collider began operation in 2019. It has since recorded half of the data collected by its predecessor, and reached a world record instantaneous luminosity of 4.7 × 1034 cm-2s-1. For distinguishing decays with missing energy from background events at Belle II, the residual calorimeter energy measured by the electromagnetic calorimeter is an important quantity. Ideally, calorimeter clusters due to beam backgrounds and fake photons should be excluded when the residual calorimeter energy is calculated, so identifying them during the analysis process is key. We present two new boosted decision tree classifiers that have been trained to identify such clusters at Belle II and distinguish them from real photons originating from collision events at the interaction point. We provide results from their application to the B → D*ℓν decay mode, and show that the distribution of residual calorimeter energy for signal events is significantly improved.
© The Authors, published by EDP Sciences, 2024
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.