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This book reviews the latest experimental results on jet physics from proton-proton collisons at the LHC. Jets allow to determine the strong coupling constant over a wide range of energies up the highest ones possible so far, and to constrain the gluon parton distribution of the proton, both of which are important uncertainties on theory predictions in general and for the Higgs boson in particular.A novel approach in this book is to categorize the examined quantities according to the types of absolute, ratio, or shape measurements and to explain in detail the advantages and differences. Including numerous illustrations and tables the physics message and impact of each observable is clearly elaborated.
Will research soon be done by artificial intelligence, thereby making human researchers superfluous? This book explains modern approaches to discovering physical concepts with machine learning and elucidates their strengths and limitations. The automation of the creation of experimental setups and physical models, as well as model testing are discussed. The focus of the book is the automation of an important step of the model creation, namely finding a minimal number of natural parameters that contain sufficient information to make predictions about the considered system. The basic idea of this approach is to employ a deep learning architecture, SciNet, to model a simplified version of a physicist's reasoning process. SciNet finds the relevant physical parameters, like the mass of a particle, from experimental data and makes predictions based on the parameters found. The author demonstrates how to extract conceptual information from such parameters, e.g., Copernicus' conclusion that the solar system is heliocentric.
Science is the most reliable means available for understanding the world around us and our place in it. But, since science draws conclusions based on limited empirical evidence, there is always a chance that a scientific inference will be incorrect. That chance, known as inductive risk, is endemic to science. Though inductive risk has always been present in scientific practice, the role of values in responding to it has only recently gained extensive attention from philosophers, scientists, and policy-makers. Exploring Inductive Risk brings together a set of eleven concrete case studies with the goals of illustrating the pervasiveness of inductive risk, assisting scientists and policymakers in responding to it, and moving theoretical discussions of this phenomenon forward. The case studies range over a wide variety of scientific contexts, including the drug approval process, high energy particle physics, dual-use research, climate science, research on gender disparities in employment, clinical trials, and toxicology. The book includes an introductory chapter that provides a conceptual introduction to the topic and a historical overview of the argument that values have an important role to play in responding to inductive risk, as well as a concluding chapter that synthesizes important themes from the book and maps out issues in need of further consideration.
This book addresses one of the most intriguing mysteries of our universe: the nature of dark matter. The results presented here mark a significant and substantial contribution to the search for new physics, in particular for new particles that couple to dark matter. The first analysis presented is a search for heavy new particles that decay into pairs of hadronic jets (dijets). This pioneering analysis explores unprecedented dijet invariant masses, reaching nearly 7 TeV, and sets constraints on several important new physics models. The two subsequent analyses focus on the difficult low dijet mass region, down to 200 GeV, and employ a novel technique to efficiently gather low-mass dijet events. The results of these analyses transcend the long-standing constraints on dark matter mediator particles set by several existing experiments.
This textbook is a unique treatise on the present status of particle physics summarised for physics students at an introductory level: it provides insights into the essential experimental and theoretical techniques needed to start research at modern high energy accelerators such as the Large Hadron Collider at CERN. The first three parts of the book discuss the experimental and phenomenological aspects at a level suitable for MSc students, but BSc students interested in particle physics will also find useful information there. The fourth part is oriented to advanced MSc or PhD students to make them acquainted with the precise formulation of the standard model of particle interactions, as well as with the mathematical background needed for the correct interpretation of the experimental results. In this two-step approach, the book offers a gradually deepening understanding of particle physics, building up the standard model and providing an overview of its verification, together with the necessary theoretical and experimental techniques. Using the example of the simplest present-day experiments, it is explained how one can obtain experimental results and theoretical estimations for measurable quantities from clear basic principles. The sources of uncertainties and the methods of improving precision are also discussed.
This thesis presents a study of the scalar sector in the standard model (SM), as well as various searches for an extended scalar sector in theories beyond the SM (BSM). The first part of the thesis details the search for an SM Higgs boson decaying to taus, and produced by gluon fusion, vector boson fusion, or associated production with a vector boson, leading to evidence for decays of the Higgs boson to taus. In turn, the second part highlights several searches for an extended scalar sector, with scalar boson decays to taus. In all of the analyses presented, at least one scalar boson decays to a pair of taus. The results draw on data collected by the Compact Muon Solenoid (CMS) detector during proton–proton collisions with a center-of-mass energy of 7 or 8 TeV.
This thesis highlights data from MINOS, a long-baseline accelerator neutrino experiment, and details one of the most sensitive searches for the sterile neutrino ever made. Further, it presents a new analysis paradigm to enable this measurement and a comprehensive study of the myriad systematic uncertainties involved in a search for a few-percent effect, while also rigorously investigating the statistical interpretation of the findings in the context of a sterile neutrino model. Among the scientific community, this analysis was quickly recognized as a foundational measurement in light of which all previous evidence for the sterile neutrino must now be (re)interpreted. The existence of sterile neutrinos has long been one of the key questions in the field. Not only are they a central component in many theories of new physics, but a number of past experiments have yielded results consistent with their existence. Nonetheless, they remain controversial: the interpretation of the data showing evidence for these sterile neutrinos is hotly debated.
The work presented in this PhD dissertation is the first search at CMS for Higgs bosons produced in association with top quarks (ttH) in a final state consisting of only jets. The results presented in this book uncover a new class of ttH events that will help us elucidate our understanding of the Yukawa sector interactions between the Higgs boson and the top quark. Despite this being the most common decay signature for ttH, a large contamination of SM backgrounds makes it the most challenging for extracting a signal from data. The PhD thesis presents many sophisticated tools and techniques that were developed in order to overcome these challenges. These tools pave the way for future analyses to investigate other standard model and beyond-standard model physics.
This primer describes the general model independent searches for new physics phenomena beyond the Standard Model of particle physics. First, the motivation for performing general model independent experimental searches for new physics is presented by giving an overview of the current theoretical understanding of particle physics in terms of the Standard Model of particle physics and its shortcomings. Then, the concept and features of general model independent search for new physics at collider based experiments is explained. This is followed by an overview of such searches performed in past high energy physics experiments and the current status of such searches, particularly in the context of the experiments at the LHC. Finally, the future prospects of such general model independent searches, with possible improvements using new tools such as machine learning techniques, is discussed.