Determination of the CKM ratio $|V_{ub}|/|V_{cb}|$ using semileptonic $B_c^{+}$ decays at LHCb

This thesis reports the first search for B_c⁺ \to D^(*)0μ⁺ \nu_μ decays using proton-proton collision data collected by the LHCb experiment operating at the Large Hadron Collider at CERN. The measurement makes use of a data sample collected between 2016 and 2018 at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 5.1 \textrm{fb}^-1. The semi-exclusive signal B_c⁺ \to D^(*)0μ⁺ \nu_μ branching fraction is measured relative to the normalisation decay, B_c⁺ \to J/\psi μ⁺ \nu_μ. The branching fraction ratio is found to be \begin{align*}\frac{\mathcal{B}(B_c⁺ \to D^(*)0μ⁺ \nu_μ)}{\mathcal{B}(B_c⁺ \to J/\psi μ⁺ \nu_μ)} &= 0.0405 \pm 0.0082 \pm 0.0065 \pm 0.0004,\end{align*} and is exploited to extract the first-ever measurement of the CKM ratio, |V_ub|/|V_cb|, using semileptonic B_c⁺ decays, \begin{align*}\frac{|V_ub|}{|V_cb|} = 0.200 \pm 0.020 \pm 0.041 \pm 0.001.\end{align*} In both results, the first uncertainty is statistical, the second is systematic, and the third is due to external measurements of the D⁰ \to K^-π⁺ and J/\psi \to μ⁺μ^- decay branching fractions. This proof-of-concept measurement demonstrates the potential for CKM metrology with the rare B_c⁺ meson. In addition, this thesis details the development of a novel deep learning architecture to perform calorimetric shower reconstruction at high energy particle physics experiments. A bespoke network, exploiting recent developments in image recognition and geometric deep learning, is designed to achieve one-shot 2D cluster reconstruction capable of learning an arbitrary detector-plane geometry. The performance of the detection network is assessed using a standalone dataset inspired by the LHCb Electromagnetic Calorimeter layout.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2022

Author dc:creator

• Delaney, Blaise

Advisor dc:contributor.advisor

• Gibson, Valerie

• B physics
• Calorimetry
• CERN
• CKM
• Geometric deep learning
• LHC
• LHCb
• Semileptonic

Licence

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Language dc:language

eng

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.87305

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/339884