Artificial Intelligence For High Energy Physics

Artificial Intelligence For High Energy Physics
Author: Paolo Calafiura
Publisher: World Scientific
Total Pages: 829
Release: 2022-01-05
Genre: Science
ISBN: 9811234043

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.

AI for Physics

AI for Physics
Author: Volker Knecht
Publisher: CRC Press
Total Pages: 149
Release: 2022-08-29
Genre: Computers
ISBN: 1000643832

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.

Deep Learning For Physics Research

Deep Learning For Physics Research
Author: Martin Erdmann
Publisher: World Scientific
Total Pages: 340
Release: 2021-06-25
Genre: Science
ISBN: 9811237476

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.

The Principles of Deep Learning Theory

The Principles of Deep Learning Theory
Author: Daniel A. Roberts
Publisher: Cambridge University Press
Total Pages: 473
Release: 2022-05-26
Genre: Computers
ISBN: 1316519333

This volume develops an effective theory approach to understanding deep neural networks of practical relevance.

Statistical Analysis Techniques in Particle Physics

Statistical Analysis Techniques in Particle Physics
Author: Ilya Narsky
Publisher: John Wiley & Sons
Total Pages: 404
Release: 2013-10-24
Genre: Science
ISBN: 3527677291

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.

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

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
Author: Denis Perret-gallix
Publisher: World Scientific
Total Pages: 802
Release: 1992-09-04
Genre: Science
ISBN: 981455426X

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.

Higgs Boson Decays into a Pair of Bottom Quarks

Higgs Boson Decays into a Pair of Bottom Quarks
Author: Cecilia Tosciri
Publisher: Springer Nature
Total Pages: 171
Release: 2021-10-22
Genre: Science
ISBN: 3030879380

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.

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

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
Author: K H Becks
Publisher: World Scientific
Total Pages: 684
Release: 1994-02-04
Genre:
ISBN: 9814551708

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.

Machine Learning at the Belle II Experiment

Machine Learning at the Belle II Experiment
Author: Thomas Keck
Publisher: Springer
Total Pages: 174
Release: 2018-12-29
Genre: Science
ISBN: 3319982494

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.