000002122 001__ 2122
000002122 005__ 20201001105034.0
000002122 037__ $$aBELLE2-MTHESIS-2020-006
000002122 041__ $$aeng
000002122 100__ $$aIlias Tsaklidis
000002122 245__ $$aDemonstrating learned particle decay reconstruction using Graph Neural Networks at BelleII
000002122 260__ $$aKarlsruhe, Strasbourg$$bKarlsruher Institut für Technologie (KIT),  Institut Pluridisciplinaire Hubert CURIEN (IPHC)$$c2020
000002122 300__ $$amult. p
000002122 500__ $$aPresented on 19 06 2020
000002122 502__ $$aMSc$$bStrasbourg, Universitè de Strasbourg$$c2020
000002122 520__ $$aThe clean environment within Belle II, with decay processes originating from non-composite electron-positron pairs, allows for the reconstruction of the entire collision event. Having precise information about the initial state kinematics gives a unique advantage to the Belle II experiment in that allows for direct measurements of decay processes involving neutrinos or few detectable particles in the final state, due to conservation of energy and momentum. This does, however, require a catch-all reconstruction algorithm which is able to determine which particles are not associated to the signal B-meson and reconstruct a second B-meson from them. The current full event reconstruction algorithm at Belle II requires the reconstructed sub-decay processes to be hard-coded, and the careful selection of which kinematic variables to exploit. This both restricts the total branching fraction coverage of the algorithm and relies on intuition to decide which decay processes to reconstruct and how to reconstruct them. This work introduces a method for learning which processes to reconstruct and how to reconstruct them from example using simple kinematic variables with graph neural networks. The efficiency of the proposed method is demonstrated by reconstructing decays from generic and Belle II phasespaces in numerous kinematic scenarios of increased complexity.
000002122 700__ $$aPablo Goldenzweig$$edir.
000002122 700__ $$aIsabelle Ripp-Baudot$$edir.
000002122 700__ $$aJames Kahn$$edir.
000002122 700__ $$aGiulio Dujany$$edir.
000002122 8560_ $$filias.tsaklidis@desy.de
000002122 8564_ $$uhttps://docs.belle2.org/record/2122/files/BELLE2-MTHESIS-2020-006.pdf
000002122 980__ $$aTHESIS