2026-09-22T01:05:02Zhttps://api.repository.cam.ac.uk/server/oai/request

oai:www.repository.cam.ac.uk:1810/3398842024-06-21T00:41:36Zcom_1810_198332com_1810_256064col_1810_214775

Determination of the CKM ratio $|V_{ub}|/|V_{cb}|$ using semileptonic $B_c^{+}$ decays at LHCb 10.17863/CAM.87305 Delaney, Blaise Gibson, Valerie This thesis reports the first search for $B_c^{+} \to D^{(*)0}\mu^+ \nu_{\mu}$ 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 $B_c^{+} \to D^{(*)0}\mu^+ \nu_{\mu}$ branching fraction is measured relative to the normalisation decay, $B_c^{+} \to J/\psi \,\mu^+ \nu_{\mu}$. The branching fraction ratio is found to be $\begin{align*}\frac{\mathcal{B}(B_c^{+} \to D^{(*)0}\mu^+ \nu_{\mu})}{\mathcal{B}(B_c^{+} \to J/\psi\,\mu^+ \nu_{\mu})} &= 0.0405 \pm 0.0082 \pm 0.0065 \pm 0.0004,\end{align*}$ 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 $D^{0} \to K^{-}\pi^{+}$ and $J/\psi \to \mu^{+}\mu^{-}$ 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. University of Cambridge 2022-01-30 Thesis Doctoral Doctor of Philosophy (PhD) eng Funded by the Science and Technology Facilities Council (STFC). https://www.repository.cam.ac.uk/handle/1810/339884 https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/ac865eb6-d935-46e7-bb06-0e9c0e60e3e0/download 5fe95f3d5de85105d3b1137acfea8343 https://creativecommons.org/licenses/by-nc-nd/4.0/ B physics Calorimetry CERN CKM Geometric deep learning LHC LHCb Semileptonic