000004148 001__ 4148
000004148 005__ 20240311041450.0
000004148 037__ $$aBELLE2-MTHESIS-2024-002
000004148 041__ $$aeng
000004148 100__ $$aCorentin Santos
000004148 245__ $$aOPTIMIZATION OF A DEEP GRAPH NEURAL NETWORK TO RECONSTRUCT GENERIC B DECAYS AT BELLE II
000004148 260__ $$aStrasbourg$$bInstitut Pluridisciplinaire Hubert Curien$$c2023
000004148 300__ $$amult. p
000004148 500__ $$aPresented on 22 06 2023
000004148 502__ $$aMSc$$bStrasbourg, Strasbourg University$$c2023
000004148 520__ $$aThis work presents the results of a fourteen weeks internship on the optimization of a deep graph neural network in the context of the search for New Physics (NP) at the Belle II experiment. The neural network is the core of an algorithm named graFEI to reconstruct B decays. A powerful probe for the search of NP is the B to K nu nubar decay channel because of its rarity and the precision of its branching ratio prediction in the Standard Model (SM). The Belle II experiment is the only experiment capable of detecting such a decay because of its cleanness and hermeticity. Moreover, the production of a partner B during e+e− collisions at the SuperKEKB collider allows to recover the missing energy from invisible particles in the final state, a technique name Full Event Reconstruction (FER), specific to Belle II. During this internship, starting from a 15% B reconstruction efficiency, the multiple optimizations performed on the graFEI get the performances to 21%. This is about 6 times the reconstruction efficiency of the main reconstruction algorithm currently used at Belle II, the Full Event Interpretation (FEI).
000004148 700__ $$aJacopo Cerasoli$$edir.
000004148 700__ $$aGiulio Dujany$$edir.
000004148 8560_ $$fcorentin.santos@iphc.cnrs.fr
000004148 8564_ $$uhttps://docs.belle2.org/record/4148/files/BELLE2-MTHESIS-2024-002.pdf
000004148 980__ $$aTHESIS