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This book introduces the reader to the field of jet substructure, starting from the basic considerations for capturing decays of boosted particles in individual jets, to explaining state-of-the-art techniques. Jet substructure methods have become ubiquitous in data analyses at the LHC, with diverse applications stemming from the abundance of jets in proton-proton collisions, the presence of pileup and multiple interactions, and the need to reconstruct and identify decays of highly-Lorentz boosted particles. The last decade has seen a vast increase in our knowledge of all aspects of the field, with a proliferation of new jet substructure algorithms, calculations and measurements which are presented in this book. Recent developments and algorithms are described and put into the larger experimental context. Their usefulness and application are shown in many demonstrative examples and the phenomenological and experimental effects influencing their performance are discussed. A comprehensive overview is given of measurements and searches for new phenomena performed by the ATLAS and CMS Collaborations. This book shows the impressive versatility of jet substructure methods at the LHC.
During Run 2 of the Large Hadron Collider, the ATLAS experiment recorded proton-proton collision events at 13 TeV, the highest energy ever achieved in a collider. Analysis of this dataset has provided new opportunities for precision measurements of the Higgs boson, including its interaction with the top quark. The Higgs-top coupling can be directly probed through the production of a Higgs boson in association with a top-antitop quark pair (ttH). The Higgs to diphoton decay channel is among the most sensitive for ttH measurements due to the excellent diphoton mass resolution of the ATLAS detector and the clean signature of this decay. Event selection criteria were developed using novel Machine Learning techniques to target ttH events, yielding a precise measurement of the ttH cross section in the diphoton channel and a 6.3 $\sigma$ observation of the ttH process in combination with other decay channels, as well as stringent limits on CP violation in the Higgs-top coupling.
The latest of the 'Lepton Photon' symposium, one of the well-established series of meetings in the high-energy physics community, was successfully organized at the South Campus of Sun Yat-sen University, Guangzhou, China, from August 7-12, 2017, where physicists around the world gathered to discuss the latest advancements in the research field.This proceedings volume of the Lepton Photon 2017 collects contributions by the plenary session speakers and the posters' presenters, which cover the latest results in particle physics, nuclear physics, astrophysics, cosmology, and plans for future facilities.
This comprehensive volume summarizes and structures the multitude of results obtained at the LHC in its first running period and draws the grand picture of today’s physics at a hadron collider. Topics covered are Standard Model measurements, Higgs and top-quark physics, flavour physics, heavy-ion physics, and searches for supersymmetry and other extensions of the Standard Model. Emphasis is placed on overview and presentation of the lessons learned. Chapters on detectors and the LHC machine and a thorough outlook into the future complement the book. The individual chapters are written by teams of expert authors working at the forefront of LHC research.
Before any kind of new physics discovery could be made at the LHC, a precise understanding and measurement of the Standard Model of particle physics' processes was necessary. The book provides an introduction to top quark production in the context of the Standard Model and presents two such precise measurements of the production of top quark pairs in proton-proton collisions at a center-of-mass energy of 7 TeV that were observed with the ATLAS Experiment at the LHC. The presented measurements focus on events with one charged lepton, missing transverse energy and jets. Using novel and advanced analysis techniques as well as a good understanding of the detector, they constitute the most precise measurements of the quantity at that time.
In an epoch when particle physics is awaiting a major step forward, the Large Hydron Collider (LHC) at CERN, Geneva will soon be operational. It will collide a beam of high energy protons with another similar beam circulation in the same 27 km tunnel but in the opposite direction, resulting in the production of many elementary particles some never created in the laboratory before. It is widely expected that the LHC will discover the Higgs boson, the particle which supposedly lends masses to all other fundamental particles. In addition, the question as to whether there is some new law of physics at such high energy is likely to be answered through this experiment. The present volume contains a collection of articles written by international experts, both theoreticians and experimentalists, from India and abroad, which aims to acquaint a non-specialist with some basic issues related to the LHC. At the same time, it is expected to be a useful, rudimentary companion of introductory exposition and technical expertise alike, and it is hoped to become unique in its kind. The fact that there is substantial Indian involvement in the entire LHC endeavour, at all levels including fabrication, physics analysis procedures as well as theoretical studies, is also amply brought out in the collection.
In this thesis, the first measurement of the running of the top quark mass is presented. This is a fundamental quantum effect that had never been studied before. Any deviation from the expected behaviour can be interpreted as a hint of the presence of physics beyond the Standard Model. All relevant aspects of the analysis are extensively described and documented. This thesis also describes a simultaneous measurement of the inclusive top quark-antiquark production cross section and the top quark mass in the simulation. The measured cross section is also used to precisely determine the values of the top quark mass and the strong coupling constant by comparing to state-of-the-art theoretical predictions. All the theoretical and experimental aspects relevant to the results presented in this thesis are discussed in the initial chapters in a concise but complete way, which makes the material accessible to a wider audience.
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 title provides an in-depth introduction to the particle physics of current and future experiments at particle accelerators. The text provides the reader with an overview of practically all aspects of the strong interaction necessary to understand and appreciate modern particle phenomenology at the energy frontier.
Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. 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. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 2 covers machine learning for knowledge discovery in particle and astroparticle physics. Their instruments, e.g., particle detectors 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.