Search for B → K𝑣𝑣 decays with a machine learning method at the Belle II experiment

Link:
Autor/in:
Beteiligte Personen:
  • Glazov, Alexander
  • Tackmann, Kerstin
Verlag/Körperschaft:
Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky
Erscheinungsjahr:
2022
Medientyp:
Text
Schlagworte:
  • Particle Physics
  • Belle II experiment
  • Rare decay
  • Electroweak penguin
  • Machine learning
  • 530: Physik
  • 33.56: Elementarteilchenphysik
  • Physik
  • Elementarteilchenphysik
  • Belle-II-Detektor
  • B-Meson
  • Maschinelles Lernen
  • ddc:530:
  • Physik
  • Elementarteilchenphysik
  • Belle-II-Detektor
  • B-Meson
  • Maschinelles Lernen
Beschreibung:
  • This thesis documents a search for the rare decay of a B meson into a K meson and a pair of neutrinos at the Belle II experiment, which is located along the SuperKEKB energy-asymmetric electron-positron collider. This decay has never been observed, its branching fraction is predicted with accuracy in the standard model of particle physics, and is a good probe of physics beyond the standard model. A novel method to search for this decay, the inclusive tagging, is developed on a data sample corresponding to an integrated luminosity of 189 fb−1 collected at the Υ(4S) resonance, and a complementary sample of 18 fb−1 collected 60 MeV below the resonance. For this integrated luminosity, the expected upper limits on the branching fraction of B+→K+νν̄ and B0→KS0νν̄ are determined from simulation to be 1.0×10−5 and 1.8×10−5 at the 90% confidence level, respectively. When the method is applied to data samples of 63 fb−1 collected at the Υ(4S) resonance and 9 fb−1 collected 60 MeV below the resonance, no significant signal is observed, and an upper limit on the branching fraction of B+→K+νν̄ is determined to be 4.1×10−5 at the 90% confidence level.
Lizenzen:
  • http://purl.org/coar/access_right/c_abf2
  • info:eu-repo/semantics/openAccess
  • https://creativecommons.org/licenses/by-nc/4.0/
Quellsystem:
E-Dissertationen der UHH

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Quelldatensatz
oai:ediss.sub.uni-hamburg.de:ediss/9793