Download or read book Artificial Intelligence For High Energy Physics written by Paolo Calafiura and published by World Scientific. This book was released on 2022-01-05 with total page 829 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Higgs boson discovery at the Large Hadron Collider in 2012 relied on boosted decision trees. Since then, high energy physics (HEP) has applied modern machine learning (ML) techniques to all stages of the data analysis pipeline, from raw data processing to statistical analysis. The unique requirements of HEP data analysis, the availability of high-quality simulators, the complexity of the data structures (which rarely are image-like), the control of uncertainties expected from scientific measurements, and the exabyte-scale datasets require the development of HEP-specific ML techniques. While these developments proceed at full speed along many paths, the nineteen reviews in this book offer a self-contained, pedagogical introduction to ML models' real-life applications in HEP, written by some of the foremost experts in their area.
Download or read book AI for Physics written by Volker Knecht and published by CRC Press. This book was released on 2022-08-29 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written in accessible language without mathematical formulas, this short book provides an overview of the wide and varied applications of artificial intelligence (AI) across the spectrum of physical sciences. Focusing in particular on AI's ability to extract patterns from data, known as machine learning (ML), the book includes a chapter on important machine learning algorithms and their respective applications in physics. It then explores the use of ML across a number of important sub-fields in more detail, ranging from particle, molecular and condensed matter physics, to astrophysics, cosmology and the theory of everything. The book covers such applications as the search for new particles and the detection of gravitational waves from the merging of black holes, and concludes by discussing what the future may hold.
Download or read book The Principles of Deep Learning Theory written by Daniel A. Roberts and published by Cambridge University Press. This book was released on 2022-05-26 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume develops an effective theory approach to understanding deep neural networks of practical relevance.
Download or read book Introduction to High Energy Physics written by Donald H. Perkins and published by Cambridge University Press. This book was released on 2000-04-13 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: This highly-regarded text provides a comprehensive introduction to modern particle physics. Extensively rewritten and updated, this 4th edition includes developments in elementary particle physics, as well as its connections with cosmology and astrophysics. As in previous editions, the balance between experiment and theory is continually emphasised. The stress is on the phenomenological approach and basic theoretical concepts rather than rigorous mathematical detail. Short descriptions are given of some of the key experiments in the field, and how they have influenced our thinking. Although most of the material is presented in the context of the Standard Model of quarks and leptons, the shortcomings of this model and new physics beyond its compass (such as supersymmetry, neutrino mass and oscillations, GUTs and superstrings) are also discussed. The text includes many problems and a detailed and annotated further reading list.
Download or read book New Computing Techniques In Physics Research Ii Proceedings Of The Second International Workshop On Software Engineering Artificial Intelligence And Expert Systems In High Energy And Nuclear Physics written by Denis Perret-gallix and published by World Scientific. This book was released on 1992-09-04 with total page 802 pages. Available in PDF, EPUB and Kindle. Book excerpt: A vivid example of the growing need for frontier physics experiments to make use of frontier technology is in the field of Artificial Intelligence (AI) and related themes.By AI we are referring here to the use of computers to deal with complex objects in an environment based on specific rules (Symbolic Manipulation), to assist groups of developers in the design, coding and maintenance of large packages (Software Engineering), to mimic human reasoning and strategy with knowledge bases to make a diagnosis of equipment (Expert Systems) or to implement a model of the brain to solve pattern recognition problems (Neural Networks). These techniques, developed some time ago by AI researchers, are confronted by down-to-earth problems arising in high-energy and nuclear physics. However, similar situations exist in other 'big sciences' such as space research or plasma physics, and common solutions can be applied.The magnitude and complexity of the experiments on the horizon for the end of the century clearly call for the application of AI techniques. Solutions are sought through international collaboration between research and industry.
Download or read book New Computing Techniques In Physics Research Iii Proceedings Of The 3rd International Workshop On Software Engineering Ai And Expert Systems For High Energy And Nuclear Physics written by K H Becks and published by World Scientific. This book was released on 1994-02-04 with total page 684 pages. Available in PDF, EPUB and Kindle. Book excerpt: No basic or applied physics research can be done nowadays without the support of computing systems, ranging from cheap personal computers to large multi-user mainframes. Some research fields like high energy physics would not exist if computers had not been invented. Departing from the more conventional numerical applications, this series of workshops has been initiated to focus on Artificial Intelligence (AI) related developments, such as symbolic manipulation for lengthy and involved algebraic computations, software engineering to assist groups of developers in the design, coding and maintenance of large packages, expert systems to mimic human reasoning and strategy in the diagnosis of equipment or neural networks to implement a model of the brain to solve pattern recognition problems. These techniques, developed some time ago by AI researchers, are confronted by down-to-earth problems arising in high-energy and nuclear physics. All this and more are covered in these proceedings.
Download or read book New Computing Techniques in Physics Research III written by Karl-Heinz Becks and published by World Scientific Publishing Company Incorporated. This book was released on 1994 with total page 664 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Higgs Boson Decays into a Pair of Bottom Quarks written by Cecilia Tosciri and published by Springer. This book was released on 2022-10-24 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The discovery in 2012 of the Higgs boson at the Large Hadron Collider (LHC) represents a milestone for the Standard Model (SM) of particle physics. Most of the SM Higgs production and decay rates have been measured at the LHC with increased precision. However, despite its experimental success, the SM is known to be only an effective manifestation of a more fundamental description of nature. The scientific research at the LHC is strongly focused on extending the SM by searching, directly or indirectly, for indications of New Physics. The extensive physics program requires increasingly advanced computational and algorithmic techniques. In the last decades, Machine Learning (ML) methods have made a prominent appearance in the field of particle physics, and promise to address many challenges faced by the LHC. This thesis presents the analysis that led to the observation of the SM Higgs boson decay into pairs of bottom quarks. The analysis exploits the production of a Higgs boson associated with a vector boson whose signatures enable efficient triggering and powerful background reduction. The main strategy to maximise the signal sensitivity is based on a multivariate approach. The analysis is performed on a dataset corresponding to a luminosity of 79.8/fb collected by the ATLAS experiment during Run-2 at a centre-of-mass energy of 13 TeV. An excess of events over the expected background is found with an observed (expected) significance of 4.9 (4.3) standard deviation. A combination with results from other \Hbb searches provides an observed (expected) significance of 5.4 (5.5). The corresponding ratio between the signal yield and the SM expectation is 1.01 +- 0.12 (stat.)+ 0.16-0.15(syst.). The 'observation' analysis was further extended to provide a finer interpretation of the V H(H → bb) signal measurement. The cross sections for the VH production times the H → bb branching ratio have been measured in exclusive regions of phase space. These measurements are used to search for possible deviations from the SM with an effective field theory approach, based on anomalous couplings of the Higgs boson. The results of the cross-section measurements, as well as the constraining of the operators that affect the couplings of the Higgs boson to the vector boson and the bottom quarks, have been documented and discussed in this thesis. This thesis also describes a novel technique for the fast simulation of the forward calorimeter response, based on similarity search methods. Such techniques constitute a branch of ML and include clustering and indexing methods that enable quick and efficient searches for vectors similar to each other. The new simulation approach provides optimal results in terms of detector resolution response and reduces the computational requirements of a standard particles simulation.
Download or read book Deep Learning For Physics Research written by Martin Erdmann and published by World Scientific. This book was released on 2021-06-25 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: A core principle of physics is knowledge gained from data. Thus, deep learning has instantly entered physics and may become a new paradigm in basic and applied research.This textbook addresses physics students and physicists who want to understand what deep learning actually means, and what is the potential for their own scientific projects. Being familiar with linear algebra and parameter optimization is sufficient to jump-start deep learning. Adopting a pragmatic approach, basic and advanced applications in physics research are described. Also offered are simple hands-on exercises for implementing deep networks for which python code and training data can be downloaded.
Download or read book Artificial Intelligence in Power System Optimization written by Weerakorn Ongsakul and published by CRC Press. This book was released on 2016-04-19 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the considerable increase of AI applications, AI is being increasingly used to solve optimization problems in engineering. In the past two decades, the applications of artificial intelligence in power systems have attracted much research. This book covers the current level of applications of artificial intelligence to the optimization problems
Download or read book Artificial Intelligence For Science A Deep Learning Revolution written by Alok Choudhary and published by World Scientific. This book was released on 2023-03-21 with total page 803 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique collection introduces AI, Machine Learning (ML), and deep neural network technologies leading to scientific discovery from the datasets generated both by supercomputer simulation and by modern experimental facilities.Huge quantities of experimental data come from many sources — telescopes, satellites, gene sequencers, accelerators, and electron microscopes, including international facilities such as the Large Hadron Collider (LHC) at CERN in Geneva and the ITER Tokamak in France. These sources generate many petabytes moving to exabytes of data per year. Extracting scientific insights from these data is a major challenge for scientists, for whom the latest AI developments will be essential.The timely handbook benefits professionals, researchers, academics, and students in all fields of science and engineering as well as AI, ML, and neural networks. Further, the vision evident in this book inspires all those who influence or are influenced by scientific progress.
Download or read book The Large Hadron Collider written by Lyndon R. Evans and published by EPFL Press. This book was released on 2009-01-01 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Describes the technology and engineering of the Large Hadron collider (LHC), one of the greatest scientific marvels of this young 21st century. This book traces the feat of its construction, written by the head scientists involved, placed into the context of the scientific goals and principles.
Download or read book The World According to Physics written by Jim Al-Khalili and published by Princeton University Press. This book was released on 2020-03-10 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scale -- Space and time -- Energy and matter -- The quantum world -- Thermodynamics and the arrow of time -- Unification -- The future of physics -- The usefulness of physics -- Thinking like a physicist.
Download or read book Universal Artificial Intelligence written by Marcus Hutter and published by Springer Science & Business Media. This book was released on 2005-12-29 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Personal motivation. The dream of creating artificial devices that reach or outperform human inteUigence is an old one. It is also one of the dreams of my youth, which have never left me. What makes this challenge so interesting? A solution would have enormous implications on our society, and there are reasons to believe that the AI problem can be solved in my expected lifetime. So, it's worth sticking to it for a lifetime, even if it takes 30 years or so to reap the benefits. The AI problem. The science of artificial intelligence (AI) may be defined as the construction of intelligent systems and their analysis. A natural definition of a system is anything that has an input and an output stream. Intelligence is more complicated. It can have many faces like creativity, solving prob lems, pattern recognition, classification, learning, induction, deduction, build ing analogies, optimization, surviving in an environment, language processing, and knowledge. A formal definition incorporating every aspect of intelligence, however, seems difficult. Most, if not all known facets of intelligence can be formulated as goal driven or, more precisely, as maximizing some utility func tion. It is, therefore, sufficient to study goal-driven AI; e. g. the (biological) goal of animals and humans is to survive and spread. The goal of AI systems should be to be useful to humans.
Download or read book Machine Learning at the Belle II Experiment written by Thomas Keck and published by Springer. This book was released on 2018-12-29 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Introducing Particle Physics written by Tom Whyntie and published by Icon Books. This book was released on 2014-06-05 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: What really happens at the most fundamental levels of nature? Introducing Particle Physics explores the very frontiers of our knowledge, even showing how particle physicists are now using theory and experiment to probe our very concept of what is real. From the earliest history of the atomic theory through to supersymmetry, micro-black holes, dark matter, the Higgs boson, and the possibly mythical graviton, practising physicist and CERN contributor Tom Whyntie gives us a mind-expanding tour of cutting-edge science. Featuring brilliant illustrations from Oliver Pugh, Introducing Particle Physics is a unique tour through the most astonishing and challenging science being undertaken today.
Download or read book Deep Learning and Physics written by Akinori Tanaka and published by Springer Nature. This book was released on 2021-03-24 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: What is deep learning for those who study physics? Is it completely different from physics? Or is it similar? In recent years, machine learning, including deep learning, has begun to be used in various physics studies. Why is that? Is knowing physics useful in machine learning? Conversely, is knowing machine learning useful in physics? This book is devoted to answers of these questions. Starting with basic ideas of physics, neural networks are derived naturally. And you can learn the concepts of deep learning through the words of physics. In fact, the foundation of machine learning can be attributed to physical concepts. Hamiltonians that determine physical systems characterize various machine learning structures. Statistical physics given by Hamiltonians defines machine learning by neural networks. Furthermore, solving inverse problems in physics through machine learning and generalization essentially provides progress and even revolutions in physics. For these reasons, in recent years interdisciplinary research in machine learning and physics has been expanding dramatically. This book is written for anyone who wants to learn, understand, and apply the relationship between deep learning/machine learning and physics. All that is needed to read this book are the basic concepts in physics: energy and Hamiltonians. The concepts of statistical mechanics and the bracket notation of quantum mechanics, which are explained in columns, are used to explain deep learning frameworks. We encourage you to explore this new active field of machine learning and physics, with this book as a map of the continent to be explored.
