G.Cowan, Statistical Data Analysis
Notes and didactic material are available at the website:
http://hep.fi.infn.it/ciulli/Site/Analisi_Dati.html
Learning Objectives
Principles of frequentist and Bayesian statistics, and applications to high energy physics.
C++ programming language and the ROOT framework.
Good knowledge of tools for data mining, in particular for data analysis in high energy experiments
Prerequisites
Courses recommended: Nuclear and subnuclear physics
Teaching Methods
CFU: 6
Contact hours for Lectures: 32
Contact hours for Laboratory-field/practice: 16
Further information
Office hours:
on demand
vitaliano.ciulli@fi.infn.it
Website: http://hep.fi.infn.it/ciulli/Site/Analisi_Dati.html
Type of Assessment
Solving of an analysis problem, including the development of a computer program, a written report and an oral discussion of proofs.
Course program
General concepts of statistics. Monte Carlo algorithms and simulations. Statistical tests and fit techniques. Unfolding. Confidence intervals and limits.
Event reconstruction in high energy collisions. Data analysis in elementary particle physics with real-life examples.