000003216 001__ 3216
000003216 005__ 20220915090956.0
000003216 037__ $$aBELLE2-MTHESIS-2022-014
000003216 041__ $$aeng
000003216 100__ $$aLea Reuter
000003216 245__ $$aFull Event Interpretation using Graph Neural Networks
000003216 260__ $$aKarlsruhe$$bInstitute of Experimental Particle Physics$$c2022
000003216 300__ $$a108
000003216 500__ $$aPresented on 17 02 2022
000003216 502__ $$aMSc$$bKarlsruhe, Karlsruhe Institute of Technology$$c2022
000003216 520__ $$aThe expected large dataset of the Belle~II experiment will enable precise measurements of rare decays to probe the Standard Model and searches for new physics. B-tagging is essential for many key measurements, as information of the  tag-side B-meson of the $\Upsilon(4\mathrm{S}) \rightarrow \mathrm{B} \bar{\mathrm{B}}$ event enables one to constrain rare signal decays which contain invisible particles such as neutrinos in the final state. The large number of possible decay channels, and hence large combinatorial space, make this a challenging task. This makes an analytical solution intractable and requires the use of multivariate methods.  A new, end-to-end trainable, graph neural network based approach was proposed in previous work, where the entire decay tree structure is encoded into a single matrix of the final state particles.  This thesis expands on the previous work and explores if this representation can be applied to Belle~II reconstructed particles. To conclude this thesis, the revised method is applied to simulated collision data of the Belle II experiment to evaluate the efficiency of this approach compared to the existing reconstruction algorithm.
000003216 700__ $$aTorben Ferber$$edir.
000003216 700__ $$aGünter Quast$$edir.
000003216 700__ $$aJames Kahn$$edir.
000003216 700__ $$aPablo Goldenzweig$$edir.
000003216 8560_ $$flea.reuter@kit.edu
000003216 8564_ $$uhttps://docs.belle2.org/record/3216/files/BELLE2-MTHESIS-2022-014.pdf
000003216 980__ $$aTHESIS