Statistical Methods for Data Analysis in Particle Physics

Author: Luca Lista

Publisher: Springer

ISBN:

Category: Science

Page: 257

View: 515

This concise set of course-based notes provides the reader with the main concepts and tools needed to perform statistical analyses of experimental data, in particular in the field of high-energy physics (HEP). First, the book provides an introduction to probability theory and basic statistics, mainly intended as a refresher from readers’ advanced undergraduate studies, but also to help them clearly distinguish between the Frequentist and Bayesian approaches and interpretations in subsequent applications. More advanced concepts and applications are gradually introduced, culminating in the chapter on both discoveries and upper limits, as many applications in HEP concern hypothesis testing, where the main goal is often to provide better and better limits so as to eventually be able to distinguish between competing hypotheses, or to rule out some of them altogether. Many worked-out examples will help newcomers to the field and graduate students alike understand the pitfalls involved in applying theoretical concepts to actual data. This new second edition significantly expands on the original material, with more background content (e.g. the Markov Chain Monte Carlo method, best linear unbiased estimator), applications (unfolding and regularization procedures, control regions and simultaneous fits, machine learning concepts) and examples (e.g. look-elsewhere effect calculation).

Data Analysis Techniques for High-Energy Physics

Author: R. K. Bock

Publisher: Cambridge University Press

ISBN:

Category: Medical

Page: 384

View: 455

Up-dated indispensable guide to handling and analysing data obtained from high-energy and nuclear physics experiments.

Statistical Analysis Techniques in Particle Physics

Fits, Density Estimation and Supervised Learning

Author: Ilya Narsky

Publisher: John Wiley & Sons

ISBN:

Category: Science

Page: 459

View: 284

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.

Statistical Problems in Particle Physics, Astrophysics and Cosmology

PHYSTAT05, Oxford, UK, 12-15 September 2005

Author: Louis Lyons

Publisher: Imperial College Press

ISBN:

Category: Science

Page: 310

View: 756

These proceedings comprise current statistical issues in analyzing data in particle physics, astrophysics and cosmology, as discussed at the PHYSTAT05 conference in Oxford. This is a continuation of the popular PHYSTAT series; previous meetings were held at CERN (2000), Fermilab (2000), Durham (2002) and Stanford (2003).In-depth discussions on topical issues are presented by leading statisticians and research workers in their relevant fields. Included are invited reviews and contributed research papers presenting the latest, state-of-the-art techniques.

Statistical Methods in Experimental Physics

Author: Frederick James

Publisher: World Scientific

ISBN:

Category: Science

Page: 345

View: 775

The first edition of this classic book has become the authoritative reference for physicists desiring to master the finer points of statistical data analysis. This second edition contains all the important material of the first, much of it unavailable from any other sources. In addition, many chapters have been updated with considerable new material, especially in areas concerning the theory and practice of confidence intervals, including the important Feldman-Cousins method. Both frequentist and Bayesian methodologies are presented, with a strong emphasis on techniques useful to physicists and other scientists in the interpretation of experimental data and comparison with scientific theories. This is a valuable textbook for advanced graduate students in the physical sciences as well as a reference for active researchers.

Data Analysis

Statistical and Computational Methods for Scientists and Engineers

Author: Siegmund Brandt

Publisher: Springer Science & Business Media

ISBN:

Category: Science

Page: 523

View: 972

The fourth edition of this successful textbook presents a comprehensive introduction to statistical and numerical methods for the evaluation of empirical and experimental data. Equal weight is given to statistical theory and practical problems. The concise mathematical treatment of the subject matter is illustrated by many examples and for the present edition a library of Java programs has been developed. It comprises methods of numerical data analysis and graphical representation as well as many example programs and solutions to programming problems. The book is conceived both as an introduction and as a work of reference. In particular it addresses itself to students, scientists and practitioners in science and engineering as a help in the analysis of their data in laboratory courses, in working for bachelor or master degrees, in thesis work, and in research and professional work.

Statistics for Nuclear and Particle Physicists

Author: Louis Lyons

Publisher: Cambridge University Press

ISBN:

Category: Science

Page: 226

View: 164

This practical approach to statistical problems arising regularly in analyzing data from nuclear and high energy physics experiments is geared toward non-statisticians.

Statistical Data Analysis

Author: Glen Cowan

Publisher: Oxford University Press

ISBN:

Category: Mathematics

Page: 197

View: 923

This book is a guide to the practical application of statistics in data analysis as typically encountered in the physical sciences. It is primarily addressed at students and professionals who need to draw quantitative conclusions from experimental data. Although most of the examples are taken from particle physics, the material is presented in a sufficiently general way as to be useful to people from most branches of the physical sciences. The first part of the book describes the basic tools of data analysis: concepts of probability and random variables, Monte Carlo techniques, statistical tests, and methods of parameter estimation. The last three chapters are somewhat more specialized than those preceding, covering interval estimation, characteristic functions, and the problem of correcting distributions for the effects of measurement errors (unfolding).

Statistical Data Analysis for the Physical Sciences

Author: Adrian Bevan

Publisher: Cambridge University Press

ISBN:

Category: Science

Page:

View: 632

Data analysis lies at the heart of every experimental science. Providing a modern introduction to statistics, this book is ideal for undergraduates in physics. It introduces the necessary tools required to analyse data from experiments across a range of areas, making it a valuable resource for students. In addition to covering the basic topics, the book also takes in advanced and modern subjects, such as neural networks, decision trees, fitting techniques and issues concerning limit or interval setting. Worked examples and case studies illustrate the techniques presented, and end-of-chapter exercises help test the reader's understanding of the material.

Computational Many-Particle Physics

Author: Holger Fehske

Publisher: Springer Science & Business Media

ISBN:

Category: Science

Page: 780

View: 107

Looking for the real state of play in computational many-particle physics? Look no further. This book presents an overview of state-of-the-art numerical methods for studying interacting classical and quantum many-particle systems. A broad range of techniques and algorithms are covered, and emphasis is placed on their implementation on modern high-performance computers. This excellent book comes complete with online files and updates allowing readers to stay right up to date.