Byron P. Roe – författare

Intended for beginning graduate students or advanced undergraduates, this text provides a thorough introduction to the phenomena of high-energy physics and the Standard Model of elementary particles. It should thus provide a sufficient introduction to the field for experimeters, as well as sufficient background for theorists to continue with advanced courses on field theory. The text develops the Standard Model from the bottom up, showing the experimental evidence for each theoretical assumption and emphasizing the most recent results. It includes thorough discussions of electromagnetic interactions (of interest in particle detection), magnetic monopoles, and extensions of the Standard Model.

Intended for advanced undergraduates and graduate students, this book is a practical guide to the use of probability and statistics in experimental physics. The emphasis is on applications and understanding, on theorems and techniques actually used in research. The text is not a comprehensive text in probability and statistics; proofs are sometimes omitted if they do not contribute to intuition in understanding the theorem. The problems, some with worked solutions, introduce the student to the use of computers; occasional reference is made to routines available in the CERN library, but other systems, such as Maple, can also be used. Topics covered include: basic concepts; definitions; some simple results independent of specific distributions; discrete distributions; the normal and other continuous distributions; generating and characteristic functions; the Monte Carlo method and computer simulations; multi-dimensional distributions; the central limit theorem; inverse probability and confidence belts; estimation methods; curve fitting and likelihood ratios; interpolating functions; fitting data with constraints; robust estimation methods. This second edition introduces a new method for dealing with small samples, such as may arise in search experiments, when the data are of low probability. It also includes a new chapter on queuing problems (including a simple, but useful buffer length example). In addition new sections discuss over- and under-coverage using confidence belts, the extended maximum-likelihood method, the use of confidence belts for discrete distributions, estimation of correlation coefficients, and the effective variance method for fitting y = f(x) when both x and y have measurement errors.

Intended for advanced undergraduates and graduate students, this book is a practical guide to the use of probability and statistics in experimental physics. The emphasis is on applications and understanding, on theorems and techniques actually used in research. The text is not a comprehensive text in probability and statistics; proofs are sometimes omitted if they do not contribute to intuition in understanding the theorem. The problems, some with worked solutions, introduce the student to the use of computers; occasional reference is made to routines available in the CERN library, but other systems, such as Maple, can also be used. Topics covered include: basic concepts; definitions; some simple results independent of specific distributions; discrete distributions; the normal and other continuous distributions; generating and characteristic functions; the Monte Carlo method and computer simulations; multi-dimensional distributions; the central limit theorem; inverse probability and confidence belts; estimation methods; curve fitting and likelihood ratios; interpolating functions; fitting data with constraints; robust estimation methods. This second edition introduces a new method for dealing with small samples, such as may arise in search experiments, when the data are of low probability. It also includes a new chapter on queuing problems (including a simple, but useful buffer length example). In addition new sections discuss over- and under-coverage using confidence belts, the extended maximum-likelihood method, the use of confidence belts for discrete distributions, estimation of correlation coefficients, and the effective variance method for fitting y = f(x) when both x and y have measurement errors.

Intended for beginning graduate students or advanced undergraduates, this text provides a thorough introduction to the phenomena of high-energy physics and the Standard Model of elementary particles. It should thus provide a sufficient introduction to the field for experimeters, as well as sufficient background for theorists to continue with advanced courses on field theory. The text develops the Standard Model from the bottom up, showing the experimental evidence for each theoretical assumption and emphasizing the most recent results. It includes thorough discussions of electromagnetic interactions (of interest in particle detection), magnetic monopoles, and extensions of the Standard Model.

Intended for beginning graduate students or advanced undergraduates, this text provides a thorough introduction to the phenomena of high-energy physics and the Standard Model of elementary particles. It should thus provide a sufficient introduction to the field for experimeters, as well as sufficient background for theorists to continue with advanced courses on field theory. The text develops the Standard Model from the bottom up, showing the experimental evidence for each theoretical assumption and emphasizing the most recent results. It includes thorough discussions of electromagnetic interactions (of interest in particle detection), magnetic monopoles, and extensions of the Standard Model.

Intended for advanced undergraduates and graduate students, this book is a practical guide to the use of probability and statistics in experimental physics. The emphasis is on applications and understanding, on theorems and techniques actually used in research. The text is not a comprehensive text in probability and statistics; proofs are sometimes omitted if they do not contribute to intuition in understanding the theorem. The problems, some with worked solutions, introduce the student to the use of computers; occasional reference is made to routines available in the CERN library, but other systems, such as Maple, can also be used. Topics covered include: basic concepts; definitions; some simple results independent of specific distributions; discrete distributions; the normal and other continuous distributions; generating and characteristic functions; the Monte Carlo method and computer simulations; multi-dimensional distributions; the central limit theorem; inverse probability and confidence belts; estimation methods; curve fitting and likelihood ratios; interpolating functions; fitting data with constraints; robust estimation methods. This second edition introduces a new method for dealing with small samples, such as may arise in search experiments, when the data are of low probability. It also includes a new chapter on queuing problems (including a simple, but useful buffer length example). In addition new sections discuss over- and under-coverage using confidence belts, the extended maximum-likelihood method, the use of confidence belts for discrete distributions, estimation of correlation coefficients, and the effective variance method for fitting y = f(x) when both x and y have measurement errors.

This book is meant to be a practical introduction into the use of probability and statistics in experimental physics for advanced undergraduate students and for graduate students. I have attempted to write a short book. It is not intended as a comprehensive text in probability and statistics. I have tried to emphasize areas I have found to be useful when doing experimental physics. Except for the first two chapters the emphasis is on applications and understanding. I have omitted proofs of formal theorems in the interests of brevity unless I felt the proof added to one''s intuition in understanding and applying the theorem. Since, however, this is a field in which there are often a number of misunderstandings, it is necessary to state some things with reasonable precision. I have tried to do this when necessary. I assume the student is familiar with partial derivatives and with ele­ mentary matrix manipulation. A computer is a needed tool for probability and statistics in experimental physics. We will introduce its use in this subject in some of the homework problems. One may interact with a computer in a batch mode or an inter­ active mode. In a batch mode, one submits FORTRAN or other language programs, the computer processes them, and returns the end results. In the interactive mode, one gives the computer an instruction, the computer processes it, indicates what it has done, and waits for the next instruction.

This book, now in its third edition, offers a practical guide to the use of probability and statistics in experimental physics that is of value for both advanced undergraduates and graduate students. Focusing on applications and theorems and techniques actually used in experimental research, it includes worked problems with solutions, as well as homework exercises to aid understanding. Suitable for readers with no prior knowledge of statistical techniques, the book comprehensively discusses the topic and features a number of interesting and amusing applications that are often neglected. Providing an introduction to neural net techniques that encompasses deep learning, adversarial neural networks, and boosted decision trees, this new edition includes updated chapters with, for example, additions relating to generating and characteristic functions, Bayes’ theorem, the Feldman-Cousins method, Lagrange multipliers for constraints, estimation of likelihood ratios, and unfolding problems.