Data Analysis in High Energy Physics: A Practical Guide to Statistical Methods - Softcover

Über die Autorin bzw. den Autor

Olaf Behnke is a staff physicist at DESY (Hamburg) and an expert for the physics of heavy quarks. He currently holds the position of physics chair in the ZEUS experiment and has recently also joined CMS.
 
Kevin Kröninger is working at the University of Göttingen on the ATLAS experiment, with a focus on top-quark physics. He is one of the authors of the Bayesian Analysis Toolkit (BAT).
 
Thomas Schörner-Sadenius is a staff scientiest at DESY, Hamburg, where he is currently the leader of the Analysis Centre of the German Helmholtz Alliance ?Physics at the Terascale?.
 
Grégory Schott is a physicist employed at the Karlsruhe Institute of Technology and works on the CMS experiment. His main contributions are in the fi eld of Higgs physics. He is one of the authors of the ROOSTATS software package.

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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 systematics and many others. Concrete applications are discussed in analysis walkthroughs. Each chapter is supplemented by numerous examples and exercises and by a list of literature and relevant links.

The book targets a broad readership at all career levels – from students to senior researchers.

From the contents:
_ Fundamental concepts
_ Parameter estimation
_ Hypothesis testing
_ Interval estimation
_ Classification
_ Unfolding
_ Constrained fits
_ How to deal with systematic uncertainties
_ Theory uncertainties
_ Statistical methods commonly used in high energy physics
_ Analysis walk-throughs
_ Applications in astronomy

Aus dem Klappentext

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 systematics and many others. Concrete applications are discussed in analysis walkthroughs. Each chapter is supplemented by numerous examples and exercises and by a list of literature and relevant links.

The book targets a broad readership at all career levels – from students to senior researchers.

From the contents:
_ Fundamental concepts
_ Parameter estimation
_ Hypothesis testing
_ Interval estimation
_ Classification
_ Unfolding
_ Constrained fits
_ How to deal with systematic uncertainties
_ Theory uncertainties
_ Statistical methods commonly used in high energy physics
_ Analysis walk-throughs
_ Applications in astronomy