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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. A complete Solutions Manual is available.
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.
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.
Second edition includes a new chapter on queuing problems and
introduces a new method for dealing with experiments in which only
a few events are observed.
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