Physics 56500 - Introduction to Elementary Particle Physics II

Lecture notes and supplementary material


Lecture 1 Jan 9Introduction to probability theory
See also, Statistics for Nuclear and Particle Phycicists by Louis Lyons
as well as Practical Statistics for Particle Physicists by Luca Lista (INFN) - arXiv:1609.04150v2
Lecture 2 Jan 11Statistics and parameter estimation
Lecture 3 Jan 16Progressive fit
Lecture 4 Jan 18Constrained minimization
Lecture 5 Jan 23Likelihood functions and confidence intervals
Lecture 6 Jan 25Binned and unbinned likelihood fits
Lecture 7 Jan 30Goodness of fit, Monte Carlo methods
See also, Monte Carlo Techniques - Particle Data Group
Lecture 8 Feb 1Template methods and unfolding
Lecture 9 Feb 6Tikhonov regularization, d'Agostini iteration
See also, TUnfold note, and user's manual.
Also, a useful set of slides...
Lecture 10 Feb 8Multivariate methods
Lecture 11 Feb 13Artificial neural networks, support vector machines
Lecture 12 Feb 15Parton distribution functions
Lecture 13 Feb 20More parton distribution functions
Lecture 14 Feb 22
Lecture 15 Feb 27
Lecture 16 Mar 1
Lecture 17 Mar 6
Lecture 18 Mar 8
Lecture 19 Mar 20
Lecture 20 Mar 22
Lecture 21 Mar 27
Lecture 22 Mar 29
Lecture 23 Apr 3
Lecture 24 Apr 5
Lecture 25 Apr 9
Lecture 26 Apr 12
Lecture 27 Apr 17
Lecture 28 Apr 19
Lecture 29 Apr 24
Lecture 30 Apr 26