Home > Books, Theses & Reports > Theses > OPTIMIZATION OF A DEEP GRAPH NEURAL NETWORK TO RECONSTRUCT GENERIC B DECAYS AT BELLE II |
Thesis | BELLE2-MTHESIS-2024-002 |
Corentin Santos ; Jacopo Cerasoli ; Giulio Dujany
2023
Institut Pluridisciplinaire Hubert Curien
Strasbourg
Abstract: This work presents the results of a fourteen weeks internship on the optimization of a deep graph neural network in the context of the search for New Physics (NP) at the Belle II experiment. The neural network is the core of an algorithm named graFEI to reconstruct B decays. A powerful probe for the search of NP is the B to K nu nubar decay channel because of its rarity and the precision of its branching ratio prediction in the Standard Model (SM). The Belle II experiment is the only experiment capable of detecting such a decay because of its cleanness and hermeticity. Moreover, the production of a partner B during e+e− collisions at the SuperKEKB collider allows to recover the missing energy from invisible particles in the final state, a technique name Full Event Reconstruction (FER), specific to Belle II. During this internship, starting from a 15% B reconstruction efficiency, the multiple optimizations performed on the graFEI get the performances to 21%. This is about 6 times the reconstruction efficiency of the main reconstruction algorithm currently used at Belle II, the Full Event Interpretation (FEI).
Note: Presented on 22 06 2023
Note: MSc
The record appears in these collections:
Books, Theses & Reports > Theses > Masters Theses