Corso per il Dottorato in Fisica (a. a. 2018-2019)

Professor: Alexis Pompili

Suggested textbooks for the theoretical part of the course:
- Cowan (ediz. 1998)

Copyright: all the material of this course could be used only under permission of the author
(pompili AT ba.infn.it) and with proper acknowledgment.

In order to connect to the virtual machine hosted at ReCas and dedicated to the course ("pompilicorso"):
- from a Unix/Linux machine : ssh -Y [username]@212.189.205.223
- from a Windows (<10) machine you need to freely download (from https://sourceforge.net) :
Xming X Server for Windows and /Xming-fonts/7.7.0.10 to be able to use Emacs)

For any problem with the VM please contact giacinto.donvito AT ba.infn.it (and put A.P. in cc)

EXERCISE 0

Introduction to ROOT : Introduction to the use of ROOT and exercises to begin with

Further material about the introduction to ROOT

Lezioni introduttive by Alfio Lazzaro: Lez.-1 , Lez.-2 , Lez.-3.
Further introductive material:
- Tutorial-1(by Manchester University)
- Tutorial-2(by Andrea Rizzi)
Online ROOT Manual

Material assembled for the PhD course.

EXERCISE 1

Introduction to RooFit

Introductive material: quick-manual(by W.Verkerke)
Lessons by W.Verkerke @ the BaBar Analysis School (2008): Lez-1 , Lez-2 , Lez-3.
Online : RooFit manual.

First Maximum Likelihood Fit with RooFit

In this exercise you will learn how to fit an invariant mass distribution (ψ'→μ⁺μ⁻) by using RooFit;
the PDF has both a signal and a background components.

For the description of the code (ROOT macro) and the procedure: Exercise-3(pdf).

For the theory behind fitting with MINUIT (the minimization engine of RooFit)
[Unbinned ML fit, Binned ML fit, Extended ML fit] have a look at the addendum Exercise-3a.
Here is additional follow-up material about MIGRAD, HESSE, MINOS functions in MINUIT.

EXERCISE 2

Refine the fit previously performed:

Firstly add the bin-by-bin pulls as a method of doing some goodness-of-fit.
Secondly let us use a (single-sided) Crystal-Ball function instead a Gaussian to describe the radiative tail.

For the description of the code (ROOT macro) and the procedure: Exercise-3b.

EXERCISE 3

In this exercise we fit the signal of a φ→K⁺K⁻ (diKaon invariant mass obtained by selecting B⁰_s→J/ψ φ in a
in a part of CMS data). In this case the experimental mass resolution (~ 1.3MeV from CMS Monte Carlo) is
smaller than the natural width of the φ (~ 4.3MeV).
Therefore the signal must be fitted with a Voigtian (it is a non-relativistic Breit-Wigner convoluted with
an experimental resolution gaussian).

For the description of the code (RooFit macro) and the procedure: Exercise-5(pdf).