Author: Giordon Stark Publisher: Springer Nature ISBN: 3030345483 Category : Science Languages : en Pages : 263
Book Description
This thesis represents one of the most comprehensive and in-depth studies of the use of Lorentz-boosted hadronic final state systems in the search for signals of Supersymmetry conducted to date at the Large Hadron Collider. A thorough assessment is performed of the observables that provide enhanced sensitivity to new physics signals otherwise hidden under an enormous background of top quark pairs produced by Standard Model processes. This is complemented by an ingenious analysis optimization procedure that allowed for extending the reach of this analysis by hundreds of GeV in mass of these hypothetical new particles. Lastly, the combination of both deep, thoughtful physics analysis with the development of high-speed electronics for identifying and selecting these same objects is not only unique, but also revolutionary. The Global Feature Extraction system that the author played a critical role in bringing to fruition represents the first dedicated hardware device for selecting these Lorentz-boosted hadronic systems in real-time using state-of-the-art processing chips and embedded systems.
Author: Simone Marzani Publisher: Springer ISBN: 3030157091 Category : Science Languages : en Pages : 210
Book Description
This concise primer reviews the latest developments in the field of jets. Jets are collinear sprays of hadrons produced in very high-energy collisions, e.g. at the LHC or at a future hadron collider. They are essential to and ubiquitous in experimental analyses, making their study crucial. At present LHC energies and beyond, massive particles around the electroweak scale are frequently produced with transverse momenta that are much larger than their mass, i.e., boosted. The decay products of such boosted massive objects tend to occupy only a relatively small and confined area of the detector and are observed as a single jet. Jets hence arise from many different sources and it is important to be able to distinguish the rare events with boosted resonances from the large backgrounds originating from Quantum Chromodynamics (QCD). This requires familiarity with the internal properties of jets, such as their different radiation patterns, a field broadly known as jet substructure. This set of notes begins by providing a phenomenological motivation, explaining why the study of jets and their substructure is of particular importance for the current and future program of the LHC, followed by a brief but insightful introduction to QCD and to hadron-collider phenomenology. The next section introduces jets as complex objects constructed from a sequential recombination algorithm. In this context some experimental aspects are also reviewed. Since jet substructure calculations are multi-scale problems that call for all-order treatments (resummations), the bases of such calculations are discussed for simple jet quantities. With these QCD and jet physics ingredients in hand, readers can then dig into jet substructure itself. Accordingly, these notes first highlight the main concepts behind substructure techniques and introduce a list of the main jet substructure tools that have been used over the past decade. Analytic calculations are then provided for several families of tools, the goal being to identify their key characteristics. In closing, the book provides an overview of LHC searches and measurements where jet substructure techniques are used, reviews the main take-home messages, and outlines future perspectives.
Author: Ikaros I Bigi Publisher: World Scientific ISBN: 9813233095 Category : Science Languages : en Pages : 354
Book Description
This book is dedicated to Lev Okun, who passed away in November 2015. He was a true pioneer in probing fundamental dynamics.The book has two objectives. First is to showcase Okun's impact for decades since 1963, when he published his remarkable book Weak Interaction of Elementary Particles. Second is to present the current progress of our scientific community in the studies of our Universe. New directions and possible future developments are discussed, often using the past as a guide. The authors mostly focus on CP asymmetries in the transitions of hadrons and leptons, but they also discuss their rare decays, and talk about axions and supersymmetry, and possible connections with dark matter, extra dimensions, baryogenesis and multiverse.This book is suitable for readers who know quantum mechanics and quantum field theories in general.
Author: Yuta Okazaki Publisher: Springer Nature ISBN: 9811965935 Category : Science Languages : en Pages : 230
Book Description
Supersymmetry (SUSY) introduces superpartners of the Standard Model (SM) particles. If their masses are typically O(100 GeV) ∼ O(TeV), a lightest neutralino can be a candidate for the dark matter, and the problem is solved by canceling the correction of the Higgs boson mass. Further, SUSY can explain the experimental result of the muon magnetic moment (g-2). This book presents a search for electroweakinos—the superpartners of the SM electroweak bosons—such as charginos and neutralinos using data at the LHC collected by the ATLAS detector. Pair-produced electroweakinos decay into the light ones and SM bosons (W/Z/h), and with the large mass difference between the heavy and light electroweakinos, the SM bosons have high momenta. In a fully hadronic final state, quarks decayed from the bosons are collimated, and can consequently be reconstructed as a single large-radius jet. This search has three advantages. The first is a statistical benefit by large branching ratios of the SM bosons. The second is to use characteristic signatures—the mass and substructure—of jets to identify as the SM bosons. The last is a small dependency on the signal model by targeting all the SM bosons. Thanks to them, the sensitivity is significantly improved compared to the previous analyses. Exclusion limits at the 95% confidence level on the heavy electroweakino mass parameter are set as a function of the light electroweakino mass parameter. They are set on wino or higgsino production models with various assumptions, such as the branching ratio of their decaying and the type of lightest SUSY particle. These limits are the most stringent limits. Besides, this book provides the most stringent constraints on SUSY scenarios motivated by the dark matter, the muon g-2 anomaly, and the naturalness.
Author: Masashi Aiko Publisher: Springer Nature ISBN: 9819913241 Category : Science Languages : en Pages : 268
Book Description
This book investigates the physics of the discovered Higgs boson and additional Higgs bosons in the extended Higgs models which includes higher-order quantum corrections. While the 125 GeV Higgs boson was discovered, the structure of the Higgs sector is still a mystery. Since the Higgs sector determines the concrete realization of the Higgs mechanism, the study of its nature is one of the central interests in current and future high-energy physics. The book begins with a review of the standard model and the two-Higgs doublet model, which is one of the representatives of the extended Higgs models. Subsequently, we discuss the studies of the two-Higgs doublet model at the lowest order of perturbation. Following the lowest-order analysis, we study the higher-order electroweak corrections in Higgs physics. After reviewing the renormalization procedure and the higher-order corrections in the decays of the discovered Higgs boson, we discuss the higher-order corrections in the Higgs strahlung process from an electron-positron collision, the decays of the additional charged and CP-odd Higgs bosons in the two-Higgs doublet model. From the series of these studies, it is found that the nature of the Higgs sector can be widely investigated by future collider experiments.
Author: Roman Pasechnik Publisher: Frontiers Media SA ISBN: 2889639908 Category : Science Languages : en Pages : 180
Book Description
This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
Author: Wei Wang Publisher: World Scientific ISBN: 9811207410 Category : Science Languages : en Pages : 576
Book Description
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.
Author: Roman Kogler Publisher: Springer Nature ISBN: 3030728587 Category : Science Languages : en Pages : 287
Book Description
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.
Author: Oliver K. Baker Publisher: BoD – Books on Demand ISBN: 0854660836 Category : Science Languages : en Pages : 282
Book Description
This book is devoted to research topics in quantum entanglement at the energy frontier of particle and nuclear physics, and important interdisciplinary collaborations with colleagues from fields outside of physics. A non-exhaustive list of examples of the latter can include mathematics, computer science, social sciences, philosophy, and how physics can interact with them in a way that supports successful outcomes. These are exciting times in the field of quantum information science, with new research results and their applications in society exhibiting themselves rather frequently. But what is even more exciting is that the frequency of these new results and their applications increases with a rapidity that will motivate new methods, new theories, new experiments, and new collaborations outside of the field that future researchers will find quite challenging.
Author: Katharina Morik Publisher: Walter de Gruyter GmbH & Co KG ISBN: 311078596X Category : Science Languages : en Pages : 364
Book Description
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.
Author: Giordon Stark
Author: Simone Marzani
Author: Ikaros I Bigi
Author: Yuta Okazaki
Author: Masashi Aiko
Author: Roman Pasechnik
Author: Wei Wang
Author: Roman Kogler
Author: Oliver K. Baker
Author: Katharina Morik