A Hierarchical NeuroBayes-based Algorithm for Full Reconstruction of B Mesons at B Factories
Michael Feindt, Fabian Keller, Michal Kreps, Thomas Kuhr, Sebastian, Neubauer, Daniel Zander, Anze Zupanc

TL;DR
This paper introduces a hierarchical, neural network-based algorithm for reconstructing B mesons at B-factories, significantly improving efficiency and flexibility over traditional cut-based methods.
Contribution
It presents a novel NeuroBayes-based hierarchical reconstruction framework that enhances B meson reconstruction efficiency and allows adjustable purity and efficiency levels.
Findings
Reconstructed 1104 decay channels using 71 neural networks.
Achieved roughly double the efficiency of classical algorithms.
Improved purity and efficiency trade-offs in B meson reconstruction.
Abstract
We describe a new B-meson full reconstruction algorithm designed for the Belle experiment at the B-factory KEKB, an asymmetric e+e- collider that collected a data sample of 771.6 x 10^6 BBbar pairs during its running time. To maximize the number of reconstructed B decay channels, it utilizes a hierarchical reconstruction procedure and probabilistic calculus instead of classical selection cuts. The multivariate analysis package NeuroBayes was used extensively to hold the balance between highest possible efficiency, robustness and acceptable consumption of CPU time. In total, 1104 exclusive decay channels were reconstructed, employing 71 neural networks altogether. Overall, we correctly reconstruct one B+/- or B0 candidate in 0.28% or 0.18% of the BBbar events, respectively. Compared to the cut-based classical reconstruction algorithm used at the Belle experiment, this is an improvement…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
