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This book discusses the study of double charm B decays and the first observation of B0->D0D0Kst0 decay using Run I data from the LHCb experiment. It also describes in detail the upgrade for the Run III of the LHCb tracking system and the trigger and tracking strategy for the LHCb upgrade, as well as the development and performance studies of a novel standalone tracking algorithm for the scintillating fibre tracker that will be used for the LHCb upgrade. This algorithm alone allows the LHCb upgrade physics program to achieve incredibly high sensitivity to decays containing long-lived particles as final states as well as to boost the physics capabilities for the reconstruction of low momentum particles.
The Standard Model (SM) of particle physics has withstood thus far every attempt by experimentalists to show that it does not describe data. We discuss the SM in some detail, focusing on the mechanism of fermion mixing, which represents one of its most intriguing aspects. We discuss how this mechanism can be tested in b-quark decays, and how b decays can be used to extract information on physics beyond the SM. We review experimental techniques in b physics, focusing on recent results and highlighting future prospects. Particular attention is devoted to recent results from b decays into a hadron, a lepton and an anti-lepton, that show discrepancies with the SM predictions — the so-called B-physics anomalies — whose statistical significance has been increasing steadily. We discuss these experiments in a detailed manner, and also provide theoretical interpretation of these results in terms of physics beyond the SM.
This book explores how machine learning can be used to improve the efficiency of expensive fundamental science experiments. The first part introduces the Belle and Belle II experiments, providing a detailed description of the Belle to Belle II data conversion tool, currently used by many analysts. The second part covers machine learning in high-energy physics, discussing the Belle II machine learning infrastructure and selected algorithms in detail. Furthermore, it examines several machine learning techniques that can be used to control and reduce systematic uncertainties. The third part investigates the important exclusive B tagging technique, unique to physics experiments operating at the Υ resonances, and studies in-depth the novel Full Event Interpretation algorithm, which doubles the maximum tag-side efficiency of its predecessor. The fourth part presents a complete measurement of the branching fraction of the rare leptonic B decay “B→tau nu”, which is used to validate the algorithms discussed in previous parts.
This thesis, which won one of the six 2015 ATLAS Thesis Awards, concerns the study of the charmonium and bottomonium bound heavy quark bound states. The first section of the thesis describes the observation of a candidate for the chi_b(3P) bottomonium states. This represented the first observation of a new particle at the LHC and its existence was subsequently confirmed by D0 and LHCb experiments. The second part of the thesis presents measurements of the prompt and non-prompt production of the chi_c1 and chi_c2 charmonium states in proton-proton collisions. These measurements are compared to several theoretical predictions and can be used to inform the development of theoretical models of quarkonium production.
This proceedings volume is devoted to a wide variety of items, both in theory and experiment, of particle physics such as tests of the Standard Model and beyond, physics at the future accelerators, neutrino and astroparticle physics, heavy quark physics, non-perturbative QCD, quantum gravity effects and cosmology. It is important that the papers in this volume reveal the present status and new developments in the above-mentioned items on the eve of a new era that starts with the Large Hadron Collider (LHC).
During more than 10 years, from 1989 until 2000, the LEP accelerator and the four LEP experiments, ALEPH, DELPHI, L3 and OPAL, have taken data for a large amount of measurements at the frontier of particle physics. The main outcome is a thorough and successful test of the Standard Model of electroweak interactions. Mass and width of the Z and W bosons were measured precisely, as well as the Z and photon couplings to fermions and the couplings among gauge bosons. The rst part of this work will describe the most important physics results of the LEP experiments. Emphasis is put on the properties of the W boson, which was my main research eld at LEP. Especially the precise determination of its mass and its couplings to the other gauge bosons will be described. Details on physics effects like Colour Reconnection and Bose-Einstein Correlations in W-pair events shall be discussed as well. A conclusive summary of the current electroweak measurements, including low-energy results, as the pillars of possible future ndings will be given. The important contributions from Tevatron, like the measurement of the top quark and W mass, will round up the present day picture of electroweak particle physics.
The book is a compilation of the most important experimental results achieved during the past 60 years at CERN - from the mid-1950s to the latest discovery of the Higgs particle. Covering the results from the early accelerators at CERN to those most recent at the LHC, the contents provide an excellent review of the achievements of this outstanding laboratory. Not only presented is the impressive scientific progress achieved during the past six decades, but also demonstrated is the special way in which successful international collaboration exists at CERN.
Machine learning is part of Artificial Intelligence since its beginning. Certainly, not learning would only allow the perfect being to show intelligent behavior. All others, be it humans or machines, need to learn in order to enhance their capabilities. In the eighties of the last century, learning from examples and modeling human learning strategies have been investigated in concert. The formal statistical basis of many learning methods has been put forward later on and is still an integral part of machine learning. Neural networks have always been in the toolbox of methods. Integrating all the pre-processing, exploitation of kernel functions, and transformation steps of a machine learning process into the architecture of a deep neural network increased the performance of this model type considerably. Modern machine learning is challenged on the one hand by the amount of data and on the other hand by the demand of real-time inference. This leads to an interest in computing architectures and modern processors. For a long time, the machine learning research could take the von-Neumann architecture for granted. All algorithms were designed for the classical CPU. Issues of implementation on a particular architecture have been ignored. This is no longer possible. The time for independently investigating machine learning and computational architecture is over. Computing architecture has experienced a similarly rampant development from mainframe or personal computers in the last century to now very large compute clusters on the one hand and ubiquitous computing of embedded systems in the Internet of Things on the other hand. Cyber-physical systems’ sensors produce a huge amount of streaming data which need to be stored and analyzed. Their actuators need to react in real-time. This clearly establishes a close connection with machine learning. Cyber-physical systems and systems in the Internet of Things consist of diverse components, heterogeneous both in hard- and software. Modern multi-core systems, graphic processors, memory technologies and hardware-software codesign offer opportunities for better implementations of machine learning models. Machine learning and embedded systems together now form a field of research which tackles leading edge problems in machine learning, algorithm engineering, and embedded systems. Machine learning today needs to make the resource demands of learning and inference meet the resource constraints of used computer architecture and platforms. A large variety of algorithms for the same learning method and, moreover, diverse implementations of an algorithm for particular computing architectures optimize learning with respect to resource efficiency while keeping some guarantees of accuracy. The trade-off between a decreased energy consumption and an increased error rate, to just give an example, needs to be theoretically shown for training a model and the model inference. Pruning and quantization are ways of reducing the resource requirements by either compressing or approximating the model. In addition to memory and energy consumption, timeliness is an important issue, since many embedded systems are integrated into large products that interact with the physical world. If the results are delivered too late, they may have become useless. As a result, real-time guarantees are needed for such systems. To efficiently utilize the available resources, e.g., processing power, memory, and accelerators, with respect to response time, energy consumption, and power dissipation, different scheduling algorithms and resource management strategies need to be developed. This book series addresses machine learning under resource constraints as well as the application of the described methods in various domains of science and engineering. Turning big data into smart data requires many steps of data analysis: methods for extracting and selecting features, filtering and cleaning the data, joining heterogeneous sources, aggregating the data, and learning predictions need to scale up. The algorithms are challenged on the one hand by high-throughput data, gigantic data sets like in astrophysics, on the other hand by high dimensions like in genetic data. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are applied to program executions in order to save resources. The three books will have the following subtopics: Volume 1: Machine Learning under Resource Constraints - Fundamentals Volume 2: Machine Learning and Physics under Resource Constraints - Discovery Volume 3: Machine Learning under Resource Constraints - Applications Volume 2 is about machine learning for knowledge discovery in particle and astroparticle physics. Their instruments, e.g., particle accelerators or telescopes, gather petabytes of data. Here, machine learning is necessary not only to process the vast amounts of data and to detect the relevant examples efficiently, but also as part of the knowledge discovery process itself. The physical knowledge is encoded in simulations that are used to train the machine learning models. At the same time, the interpretation of the learned models serves to expand the physical knowledge. This results in a cycle of theory enhancement supported by machine learning.