This thesis reports on the final measurement of the flavor-mixing phase in decays of strange-bottom mesons (B_s) into J/psi and phi mesons performed in high-energy proton-antiproton collisions recorded by the Collider Experiment at Fermilab. Interference occurs between direct decays and decays following virtual particle-antiparticle transitions (B_s-antiB_s). The phase difference between transition amplitudes (“mixing phase”) is observable and extremely sensitive to contributions from non-standard-model particles or interactions that may be very hard to detect otherwise – a fact that makes the precise measurement of the B_s mixing phase one of the most important goals of particle physics. The results presented include a precise determination of the mixing phase and a suite of other important supplementary results. All measurements are among the most precise available from a single experiment and provide significantly improved constraints on the phenomenology of new particles and interactions.

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 is the first advanced, systematic and comprehensive look at weak decays in the framework of gauge theories. Included is a large spectrum of topics, both theoretical and experimental. In addition to explicit advanced calculations of Feynman diagrams and the study of renormalization group strong interaction effects in weak decays, the book is devoted to the Standard Model Effective Theory, dominating present phenomenology in this field, and to new physics models with the goal of searching for new particles and interactions through quantum fluctuations. This book will benefit theorists, experimental researchers, and Ph.D. students working on flavour physics and weak decays as well as physicists interested in physics beyond the Standard Model. In its concern for the search for new phenomena at short distance scales through the interplay between theory and experiment, this book constitutes a travel guide to physics far beyond the scales explored by the Large Hadron Collider at CERN.

This work covers the required mathematical and theoretical tools required for understanding the Standard Model of particle physics. It explains the accelerator and detector physics which are needed for the experiments that underpin the Standard Model.

This first open access volume of the handbook series contains articles on the standard model of particle physics, both from the theoretical and experimental perspective. It also covers related topics, such as heavy-ion physics, neutrino physics and searches for new physics beyond the standard model. A joint CERN-Springer initiative, the "Particle Physics Reference Library" provides revised and updated contributions based on previously published material in the well-known Landolt-Boernstein series on particle physics, accelerators and detectors (volumes 21A, B1,B2,C), which took stock of the field approximately one decade ago. Central to this new initiative is publication under full open access

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.