Physics case for an LHCb Upgrade II - Opportunities in flavour physics, and beyond, in the HL-LHC era

The LHCb Upgrade II will fully exploit the flavour-physics opportunities of the HL-LHC, and study additional physics topics that take advantage of the forward acceptance of the LHCb spectrometer. The LHCb Upgrade I will begin operation in 2020. Consolidation will occur, and modest enhancements of the Upgrade I detector will be installed, in Long Shutdown 3 of the LHC (2025) and these are discussed here. The main Upgrade II detector will be installed in long shutdown 4 of the LHC (2030) and will build on the strengths of the current LHCb experiment and the Upgrade I. It will operate at a luminosity up to 2×1034 cm−2s−1, ten times that of the Upgrade I detector. New detector components will improve the intrinsic performance of the experiment in certain key areas. An Expression Of Interest proposing Upgrade II was submitted in February 2017. The physics case for the Upgrade II is presented here in more depth. CP-violating phases will be measured with precisions unattainable at any other envisaged facility. The experiment will probe b → sl+l−and b → dl+l− transitions in both muon and electron decays in modes not accessible at Upgrade I. Minimal flavour violation will be tested with a precision measurement of the ratio of B(B0 → μ+μ−)/B(Bs → μ+μ−). Probing charm CP violation at the 10−5 level may result in its long sought discovery. Major advances in hadron spectroscopy will be possible, which will be powerful probes of low energy QCD. Upgrade II potentially will have the highest sensitivity of all the LHC experiments on the Higgs to charm-quark couplings. Generically, the new physics mass scale probed, for fixed couplings, will almost double compared with the pre-HL-LHC era; this extended reach for flavour physics is similar to that which would be achieved by the HE-LHC proposal for the energy frontier

LHCb upgrade software and computing : technical design report

This document reports the Research and Development activities that are carried out in the software and computing domains in view of the upgrade of the LHCb experiment. The implementation of a full software trigger implies major changes in the core software framework, in the event data model, and in the reconstruction algorithms. The increase of the data volumes for both real and simulated datasets requires a corresponding scaling of the distributed computing infrastructure. An implementation plan in both domains is presented, together with a risk assessment analysis

Test de l'universalité de la saveur leptonique en utilisant les désintégrations B0 en D* tau nu à LHCb

This thesis presents the measurement of the \rdstar \equiv \frac{BR(B^0 \to D^{*-} \tau^+ \nu_\tau})}{BR(B^0 \to D^{*-} \mu^+ \nu_mu})} ratio with 2\invfb of $pp$ collisions collected at \sqs=13\tev by \lhcb during 2015-2016 using 3-prong tau decays. The study comprises a test of the Lepton Flavour Universality in \btoclnu decays to help resolve the tension between the Standard Model $R(D^*)$estimation and the experimental results from the B-factories and LHCb.The analysis builds upon a previous LHCb measurement with a new dataset and improved techniques.Most importantly, a novel fast simulation technique, ReDecay, is used to generate large simulated samples andMultivariate Analysis techniques are exploited in signal selection.Since the analysis has yet to undergo an internal LHCb review, and several systematic uncertainties must be computed, the $R(D^*)$ value is blinded.Nonetheless, nearly complete documentation of the analysis is presented in this thesis.The current relative statistical uncertainty on the $R(D^*)$is 5.56\%.Cette thèse présente la mesure du rapport R(D^*) = \equiv \frac{BR(B^0 \to D^{*-} \tau^+ \nu_\tau})}{BR(B^0 \to D^{*-} \mu^+ \nu_mu})}avec les données accumulées par l'expérience LHCb en 2015 et 2016, en utilisant les désintégrations du tau en trois pions.L'étude comprend un test de l'universalité de la saveur leptonique dans les désintégrations $b \to c \ell \nu$ pour aider à résoudre la tension entre l'estimation de $R(D^*)$ dans le Modèle Standard et les résultats expérimentaux des usines à $B$ et de LHCb.L'analyse s'appuie sur une mesure précédente effectuée dans LHCb avec un nouvel ensemble de données et des techniques améliorées.En particulier, une nouvelle technique de simulation rapide, ReDecay, est utilisée pour générer de nombreux échantillons simulés ainsi queles techniques d'analyses multivariées sont exploitées dans la sélection des candidats.Étant donné que l'analyse n'a pas encore fait l'objet d'une revue interne dans la collaboration LHCb et que plusieurs incertitudes systématiques doivent être calculées, la valeur du $R(D^*)$ est cach\'ee.Néanmoins, une documentation presque complète de l'analyse est présentée dans cette thèse.L'incertitude statistique relative sur \rdstarest \'egale actuellement \`a 5,56\%

Test de l'universalité de la saveur leptonique avec l'expérience LHCB au LHC

Cette thèse présente la mesure du rapport R(D^{*}) \equiv \frac{\mathcal{B}(B^{0} \rightarrow D^{*} \tau \nu}{\mathcal{B}(B^{0} \rightarrow D^{*} \mu \nu} avec les données accumulées par l'expérience LHCb en 2015 et 2016, en utilisant les désintégrations du tau en trois pions. L'étude comprend un test de l'universalité de la saveur leptonique dans les désintégrations b \rightarrow c l \nu pour aider à résoudre la tension entre l'estimation de R(D^{*}) dans le Modèle Standard et les résultats expérimentaux des usines à B et de LHCb. L'analyse s'appuie sur une mesure précédente LHCb (LHCb-PAPER-2017-017, LHCb-PAPER-2017-027) avec un nouvel ensemble de données et des techniques améliorées. En particulier, une nouvelle technique de simulation rapide, ReDecay, est utilisée pour générer de nombreux échantillons simulés ainsi que les techniques d'analyses multivariées sont exploitées dans la sélection des candidats. Étant donné que l'analyse n'a pas encore fait l'objet d'une revue interne dans la collaboration LHCb et que plusieurs incertitudes systématiques doivent être calculées, la valeur du R(D^{*}) est cachée. Néanmoins, une documentation presque complète de l'analyse est présentée dans cette thèse. L'incertitude statistique relative du résultat préliminaire est de 5,4%, ce qui représente une amélioration par rapport à la mesure précédente à LHCb.This thesis presents the measurements of the R(D^{*}) \equiv \frac{\mathcal{B}(B^{0} \rightarrow D^{*} \tau \nu}{\mathcal{B}(B^{0} \rightarrow D^{*} \mu \nu} ratio with the 2015-2016 LHCb dataset using 3-prong tau decays. The study comprises a test of the Lepton Flavour Universality in b \rightarrow c l \nu decays to help resolve the tension between the Standard Model R(D^{*}) estimation and the experimental results from the B-factories and LHCb. The analysis builds upon a previous LHCb measurement (LHCb-PAPER-2017-017,LHCb-PAPER-2017-027) with a new dataset and improved techniques. Most importantly, a novel fast simulation technique, ReDecay, is used to generate large simulated samples and Multivariate Analysis techniques are exploited in signal selection. Since the analysis has yet to undergo an internal LHCb review, and several systematic uncertainties must be computed, the R(D^{*}) value is blinded. Nonetheless, nearly complete documentation of the analysis is presented in this thesis. The relative statistical uncertainty of the preliminary result is 5.4%, which is an improvement with respect to the previous 3-prong measurement at LHCb

Test of Lepton Flavour Universality using the B0→D∗−τ+ντB^0 \to D^{*-} \tau^+ \nu_\tau decays at LHCb

This thesis presents the measurement of the $R$($D$$^{*}$) $\equiv$ $\frac{B(B^0 \to D^{*-} \tau^{+}\nu_\tau}{B(B^0 \to D^{*-} \mu^{+}\nu_\mu}$ ratio with 2 fb$^{-1}$ of $pp$ collisions collected at $\sqrt s=13 \text{TeV}$ by LHCb during 2015-2016 using 3-prong tau decays. The study comprises a test of the Lepton Flavour Universality in $b$ $\to$ $c$$\ell$$\nu$$_{\ell}$ decays to help resolve the tension between the Standard Model $R(D^*)$ estimation and the experimental results from the B-factories and LHCb. The analysis builds upon a previous LHCb measurement with a new dataset and improved techniques. Most importantly, a novel fast simulation technique, ReDecay, is used to generate large simulated samples and Multivariate Analysis techniques are exploited in signal selection. Since the analysis has yet to undergo an internal LHCb review, and several systematic uncertainties must be computed, the $R(D^*)$ value is blinded. Nonetheless, nearly complete documentation of the analysis is presented in this thesis. The current relative statistical uncertainty on the $R(D^*)$ is 5.56 %

Testowanie prototypowych pikselowych sensorów hybrydowych dla zmodernizowanego detektora wierzchołka eksperymentu LHCb

praca inżyniersk

Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology

Wearable accelerometers are widely used as an ecologically valid and scalable solution for long-term at-home sleep monitoring in both clinical research and care. In this study, we applied a deep learning domain adversarial convolutional neural network (DACNN) model to this task and demonstrated that this new model outperformed existing sleep algorithms in classifying sleep–wake and estimating sleep outcomes based on wrist-worn accelerometry. This model generalized well to another dataset based on different wearable devices and activity counts, achieving an accuracy of 80.1% (sensitivity 84% and specificity 58%). Compared to commonly used sleep algorithms, this model resulted in the smallest error in wake after sleep onset (MAE of 48.7, Cole–Kripke of 86.2, Sadeh of 108.2, z-angle of 57.5) and sleep efficiency (MAE of 11.8, Cole–Kripke of 18.4, Sadeh of 23.3, z-angle of 9.3) outcomes. Despite being around for many years, accelerometer-alone devices continue to be useful due to their low cost, long battery life, and ease of use. Improving the accuracy and generalizability of sleep algorithms for accelerometer wrist devices is of utmost importance. We here demonstrated that domain adversarial convolutional neural networks can improve the overall accuracy, especially the specificity, of sleep–wake classification using wrist-worn accelerometer data, substantiating its use as a scalable and valid approach for sleep outcome assessment in real life