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Artificial Intelligence For High Energy Physics
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Book Synopsis Artificial Intelligence For High Energy Physics by : Paolo Calafiura
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 149 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.
Book Synopsis Deep Learning For Physics Research by : Martin Erdmann
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.
Book Synopsis The Principles of Deep Learning Theory by : Daniel A. Roberts
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.
Book Synopsis 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 by : Denis Perret-gallix
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.
Book Synopsis New Computing Techniques in Physics Research III by : Karl-Heinz Becks
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:
Book Synopsis Statistical Analysis Techniques in Particle Physics by : Ilya Narsky
Download or read book Statistical Analysis Techniques in Particle Physics written by Ilya Narsky and published by John Wiley & Sons. This book was released on 2013-10-24 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern analysis of HEP data needs advanced statistical tools to separate signal from background. This is the first book which focuses on machine learning techniques. It will be of interest to almost every high energy physicist, and, due to its coverage, suitable for students.
Book Synopsis 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 by : K H Becks
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.
Book Synopsis Higgs Boson Decays into a Pair of Bottom Quarks by : Cecilia Tosciri
Download or read book Higgs Boson Decays into a Pair of Bottom Quarks written by Cecilia Tosciri and published by Springer Nature. This book was released on 2021-10-22 with total page 171 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.
Book Synopsis Machine Learning at the Belle II Experiment by : Thomas Keck
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.
Book Synopsis Artificial Intelligence on Dark Matter and Dark Energy by : Ariel Fernández
Download or read book Artificial Intelligence on Dark Matter and Dark Energy written by Ariel Fernández and published by CRC Press. This book was released on 2023-08-24 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: As we prod the cosmos at very large scales, basic tenets of physics seem to crumble under the weight of contradicting evidence. This book helps mitigate the crisis. It resorts to artificial intelligence (AI) for answers and describes the outcome of this quest in terms of an ur-universe, a quintessential compact multiply connected space that incorporates a fifth dimension to encode space-time as a latent manifold. In some ways, AI is bolder than humans because the huge corpus of knowledge, starting with the prodigious Standard Model (SM) of particle physics, poses almost no burden to its conjecture-framing processes. Why not feed AI with the SM enriched by the troubling cosmological phenomenology on dark matter and dark energy and see where AI takes us vis-à-vis reconciling the conflicting data with the laws of physics? This is precisely the intellectual adventure described in this book and – to the best of our knowledge – in no other book on the shelf. As the reader will discover, many AI conjectures and validations ultimately make a lot of sense, even if their boldness does not feel altogether "human" yet. This book is written for a broad readership. Prerequisites are minimal, but a background in college math/physics/computer science is desirable. This book does not merely describe what is known about dark matter and dark energy but also provides readers with intellectual tools to engage in a quest for the deepest cosmological mystery.
Book Synopsis Artificial Intelligence For Science: A Deep Learning Revolution by : Alok Choudhary
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.
Book Synopsis New Computing Techniques in Physics Research IV by : B. Denby
Download or read book New Computing Techniques in Physics Research IV written by B. Denby and published by World Scientific Publishing Company Incorporated. This book was released on 1995 with total page 793 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Data Analysis in High Energy Physics by : Olaf Behnke
Download or read book Data Analysis in High Energy Physics written by Olaf Behnke and published by John Wiley & Sons. This book was released on 2013-08-30 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical guide covers the essential tasks in statistical data analysis encountered in high energy physics and provides comprehensive advice for typical questions and problems. The basic methods for inferring results from data are presented as well as tools for advanced tasks such as improving the signal-to-background ratio, correcting detector effects, determining systematics and many others. Concrete applications are discussed in analysis walkthroughs. Each chapter is supplemented by numerous examples and exercises and by a list of literature and relevant links. The book targets a broad readership at all career levels - from students to senior researchers. An accompanying website provides more algorithms as well as up-to-date information and links. * Free solutions manual available for lecturers at www.wiley-vch.de/supplements/
Book Synopsis An Introduction to the Physics of High Energy Accelerators by : D. A. Edwards
Download or read book An Introduction to the Physics of High Energy Accelerators written by D. A. Edwards and published by John Wiley & Sons. This book was released on 2008-11-20 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first half deals with the motion of a single particle under the influence of electronic and magnetic fields. The basic language of linear and circular accelerators is developed. The principle of phase stability is introduced along with phase oscillations in linear accelerators and synchrotrons. Presents a treatment of betatron oscillations followed by an excursion into nonlinear dynamics and its application to accelerators. The second half discusses intensity dependent effects, particularly space charge and coherent instabilities. Includes tables of parameters for a selection of accelerators which are used in the numerous problems provided at the end of each chapter.
Book Synopsis Lost in Math by : Sabine Hossenfelder
Download or read book Lost in Math written by Sabine Hossenfelder and published by Basic Books. This book was released on 2018-06-12 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this "provocative" book (New York Times), a contrarian physicist argues that her field's modern obsession with beauty has given us wonderful math but bad science. Whether pondering black holes or predicting discoveries at CERN, physicists believe the best theories are beautiful, natural, and elegant, and this standard separates popular theories from disposable ones. This is why, Sabine Hossenfelder argues, we have not seen a major breakthrough in the foundations of physics for more than four decades. The belief in beauty has become so dogmatic that it now conflicts with scientific objectivity: observation has been unable to confirm mindboggling theories, like supersymmetry or grand unification, invented by physicists based on aesthetic criteria. Worse, these "too good to not be true" theories are actually untestable and they have left the field in a cul-de-sac. To escape, physicists must rethink their methods. Only by embracing reality as it is can science discover the truth.
Book Synopsis Machine Learning Meets Quantum Physics by : Kristof T. Schütt
Download or read book Machine Learning Meets Quantum Physics written by Kristof T. Schütt and published by Springer Nature. This book was released on 2020-06-03 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.
