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Thursday, July 23, 2020 | History

9 edition of Data analysis techniques for high-energy physics. found in the catalog.

Data analysis techniques for high-energy physics.

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Published by Cambridge University Press in Cambridge, U.K, New York .
Written in English

    Subjects:
  • Particles (Nuclear physics) -- Experiments -- Data processing

  • Edition Notes

    Other titlesData analysis techniques for high-energy physics experiments.
    SeriesCambridge monographs on particle physics, nuclear physics, and cosmology ;, 11
    ContributionsFrühwirth, R., Regler, M.
    Classifications
    LC ClassificationsQC793.412 .D37 2000
    The Physical Object
    Paginationxxiv, 384 p. :
    Number of Pages384
    ID Numbers
    Open LibraryOL35418M
    ISBN 100521632196, 0521635489
    LC Control Number99020207

    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 . Up-dated indispensable guide to handling and analysing data obtained from high-energy and nuclear physics experiments. ISBN (This book at Amazon) (print version, hardback) (This book at Amazon) (print version, paperback) (This book at Amazon) (print version, paperback) (This book at Amazon.

    The explanation of how one carries out the data analysis process is an area that is sadly neglected by many researchers. This paper presents a variety of data analysis techniques . This practical guide covers the most essential statistics-related tasks and problems encountered in high-energy physics data analyses. It addresses both advanced students entering the field of particle physics as well as researchers looking for a reliable source on optimal separation of signal and background, determining signals or estimating upper limits, correcting the data for .

    © Cambridge University Press Cambridge University Press - Data Analysis Techniques for High-Energy Physics, Second Edition. observe basic techniques of data analysis to real-life Head Start examples; and identify and articulate trends and patterns in data gathered over time. Guiding Principles for Approaching Data Analysis 1. To provide information to program staff from a variety of different backgrounds and levels of prior experience. 2.


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Data analysis techniques for high-energy physics Download PDF EPUB FB2

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 /5(6).

High-energy physics - the science of the fundamental particles nature - has become one of the most complex and demanding disciplines of natural science.

The authors have written this book both as an introduction and to inform the reader on the most advanced techniques of data analysis in this by:   Data Analysis Techniques for High-Energy Physics by M. Regler,available at Book Depository with free delivery worldwide/5(2).

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.

Now thoroughly revised and up-dated, this book describes techniques for handling and analysing data obtained from high-energy and nuclear physics experiments.

The observation of particle interactions involves the analysis of large and complex data samples. Beginning with a chapter on real-time data triggering and filtering, the book describes methods of selecting the. adshelp[at] The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative Agreement NNX16AC86ACited by:   Pattern recognition; 3.

Track and vertex fitting; 4. Tools and concepts for data analysis; References; Index. Preface; Abbreviations; Symbols; Intoduction; 1. Real-time data triggering and filtering; 2. Pattern recognition; 3. Data Analysis Techniques for High-Energy Physics .F full text by:   Now thoroughly revised and up-dated, this volume describes techniques for handling and analyzing data obtained from high-energy and nuclear physics experiments.

Beginning with a chapter on real-time data triggering and filtering, the book describes methods of selecting the relevant events from a large by:   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. data samples. In order to obtain physics results from such large amounts of data, methods based on advanced statistics are extensively applied. In recent years, due to the continuous development in computing, several statistical methods became easily available for data analysis, and several packages have been developed aiming to provide.

Data Analysis and Statistical Methods in Experimental Particle Physics Thomas’R.’Junk’ Fermilab) University’of’D0’Lecture’Series.’’Nov.’8,’ Find helpful customer reviews and review ratings for Data Analysis in High Energy Physics: A Practical Guide to Statistical Methods at Read honest and unbiased product reviews from our users.

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. Statistical Analysis Techniques in Particle Physics por Ilya Narsky,disponible en Book Depository con envío gratis. Now thoroughly revised and up-dated, this book describes techniques for handling and analysing data obtained from high-energy and nuclear physics experiments.

The observation of particle interactions involves the analysis of large and complex data samples. Data Analysis in High Energy Physics: A Practical Guide to Statistical Methods - Kindle edition by Behnke, Olaf, Kröninger, Kevin, Schott, Grégory, Schörner-Sadenius, Thomas.

Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Data Analysis in High Energy Physics /5(6). Get this from a library. Data analysis techniques for high-energy physics.

[R Frühwirth; M Regler;] -- "Now thoroughly revised and up-dated, this book describes techniques for handling and analysing data obtained from high-energy and nuclear physics experiments." "This guide will appeal to graduate.

Our modern information age leads to dynamic and extremely high growth of the data mining world. No doubt, that it requires adequate and effective different types of data analysis methods, techniques, and tools that can respond to constantly increasing business research needs.

In fact, data mining does not have its own methods of data analysis. The following book is a guide to the practical application of statistics in data analysis as typically encountered in the physical sciences, and in particular in high energy particle physics.

Students entering this field do not usually go through a formal course in. Data Analysis in High Energy Physics: A Practical Guide to Statistical Methods eBook: Behnke, Olaf, Kröninger, Kevin, Schott, Grégory, Schörner-Sadenius, Thomas Reviews: 5.

This is a great text book for any graduate student (or senior researcher) working on data analysis in nuclear or high energy physics. Most of the current techniques are covered well, so the reader understands not only how to use the method, but also the assumptions and applicability of the by: 3.

What is Data Analysis? Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis.Cambridge Monographs on Particle Physics, Nuclear Physics and Cosmology Ser.: Data Analysis Techniques for High-Energy Physics 11 (2nd Edition) by R.

K. Bock, H. Grote, R. Frühwirth, D. Notz, R. Fruhwirth, M. Regler Hardcover, Pages, Published ISBN / ISBN / Need it Fast?

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