For any problem with the VM please contact vincenzo.spinoso AT ba.infn.it (and put A.P. in cc)
Online ROOT Web Page (the new frontier to read big data: RDataFrame)
Online ROOT DOCS references
In this exercise you will learn, starting by a given rootuple of histograms, how to get the plots in Fig.4 (or Fig.6)
in the CMS paper JINST 7 P10002 (2012).
To understand the physics content (muon reconstruction and identification at CMS) please study the pagg. 6-14.
For the description of the code (ROOT macro) and the procedure: Esercitazione-1
Additional code concerns how-to-do simulation-to-data ratio;
proper rebinning can be suitable to make the ratio not prone to fluctuations in the distribution tails (see exercise-with-solution)
Simple exercise for home: try relative normalization (shape comparison) instead of absolute normalization as proposed in the main code exercise.
Please checkout the following physics note.
D^0 meson production cross section:
- CMS data compared with FONLL (https://arxiv.org/abs/2107.01476 [JHEP 11 (2021) 225]; Figure 5 / upper);
- CMS data compared with ALICE data (https://arxiv.org/abs/2106.08278 [PRL 120 (2022) 012001]).
For both tasks: Esercitazione-2 .
Learn the use of TGraphErrors and TGraphAsymErrors.
For the description of the code (ROOT macro) and the procedure: Esercitazione-3
For the description of the code (ROOT macro) and the procedure: Exercise-4.
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.
Here is additional follow-up material about MIGRAD, HESSE, MINOS functions in MINUIT.
Exercise: enable MINOS and check the difference with the symmetric(parabolic) error estimations.
For the description of the code (ROOT macro) and the procedure: Exercise-4b.
These slides introduce the single-sided CB implementation, the bin-by-bin pulls and their uncertainty.
Exercise-4c: discuss why the projection of the bin-by-bin pulls should follow a standard gaussian distribution.
If not then the fit has something "pathological".
This exercise relies upon the fits learned in Exercise 4 but here you have to automatize the fits in all the rapidity bins
in order to try get, by means of a final fit, a functional expression that represents the variation of the mass resolution with the rapidity.
Here you can find all the info needed to carry out the Exercise-5.
This exercise relies upon the things learned in Exercise 4.
It has been given as an Exam in 2014/15.
Here is proposed as an exercise to start in the classroom (and finish at home if needed).
Here you are asked to fit the signal of ψ' decaying into μ+μ −π +π −.
Check the mass resolution with respect to the previous signal of ψ' decaying into 2 muons
and appreciate how much it enhances with two more tracks (pions) constrained to come from the decay vertex.
Here you can find the outline of this Exercise-6.
Use all what learned before including pulls and interpolation models.
In this exercise we fit the signal of a φ→K+K −
(diKaon invariant mass obtained by selecting B0s→J/ψ φ in a
in a part of CMS open 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-7.
Some RooFit documentation for the Profile Likelihood Ratio: supplementary material (mostly used in my slides).
Try two different background models (Chebyshev and Exponential);
try also to use a common Alpha and N for the tails of the two Crystal Ball functions.
Warning: find a turnaround for the maximum of 9 arguments in RooArgList
Generation of a distribution according to a signal+background model and subsequent fit by means of an UML fit
(with RooFit) [the signal is a Voigtian (BW convoluted with a gaussian resolution function)
while the background is given an exponential behaviour].
For the description of the code (RooFit macro) and the procedure: Exercise-10.
To be able to set the seed of the random generator please see this Additional Note.
Here you can find a follow-up: supplementary material.
By injecting a signal on a background distribution we learn how to evaluate the local statistical significance of the signal
in 3 different equivalent ways : Exercise11-part1.
The slides contain a detailed discussion of the theory following the paper by Cowan, Kranmer, Gross and Vitells Eur.Phys.J. C 71 (2011) 1554.
On the path of the previous exercise we inject a stronger signal and we discuss the practical definition of 3 Figures Of Merit
used typically in the selections (signal significance, signal purity and signal-to-noise ratio: Exercise11-part2.
Here is proposed a similtaneous interpolation of two variables (mass and proper time, for B+ to J/psi K+ selected candidates) Exercise-12
By exploiting Jupyter Hub installed on the VM we learn how to build up the extraction of the B0s to J/psi Phi signal from part of CMS open data by using a python-based notebook.