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The first book written specifically with physicists in mind on analysis techniques in particle physics with an emphasis on machine learning techniques.
Based on lectures given by the authors at Stanford and Caltech, this practical approach shows by means of analysis examples how observables are extracted from data, how signal and background are estimated, and how accurate error estimates are obtained exploiting uni- and multivariate analysis techniques, such as non-parametric density estimation, likelihood fits, neural networks, support vector machines, decision trees, and ensembles of classifiers. It includes simple code snippets that run on popular software suites such as Root and Matlab, and either include the codes for generating data or make use of publically available data that can be downloaded from the Web.
Primarily aimed at master and very advanced undergraduate students, this text is also intended for study and research.
About the author
The authors are experts in the use of statistics in particle physics data analysis. Frank C. Porter is Professor at Physics at the California Institute of Technology and has lectured extensively at CalTech, the SLAC Laboratory at Stanford, and elsewhere. Ilya Narsky is Senior Matlab Developer at The MathWorks, a leading developer of technical computing software for engineers and scientists, and the initiator of the StatPatternRecognition, a C++ package for statistical analysis of HEP data. Together, they have taught courses for graduate students and postdocs.
Summary
The first book written specifically with physicists in mind on analysis techniques in particle physics with an emphasis on machine learning techniques.
Based on lectures given by the authors at Stanford and Caltech, this practical approach shows by means of analysis examples how observables are extracted from data, how signal and background are estimated, and how accurate error estimates are obtained exploiting uni- and multivariate analysis techniques, such as non-parametric density estimation, likelihood fits, neural networks, support vector machines, decision trees, and ensembles of classifiers. It includes simple code snippets that run on popular software suites such as Root and Matlab, and either include the codes for generating data or make use of publically available data that can be downloaded from the Web.
Primarily aimed at master and very advanced undergraduate students, this text is also intended for study and research.
