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= Student Project BND - Spa September 2019 =

== Pre-requisites ==

To successfully run this tutorial the following prerequisite packages should be installed:

- gfortran/gcc/tcl:

For linux users gcc/tcl should be already installed. For Mac users you should install XCode.

- ROOT:

can be downloaded from https://root.cern.ch/downloading-root
Go on latest release, and download a version under "Binary distributions".

- MadGraph5

a version of MG5 can be downloaded here: 
https://launchpad.net/mg5amcnlo/2.0/2.5.x/+download/MG5_aMC_v2.5.5.tar.gz

- Delphes

start from here:
https://cp3.irmp.ucl.ac.be/projects/delphes/wiki/WorkBook/QuickTour

- Pythia8:

following instructions from here (or using the Pythia8 installation in !MadGraph):

https://cp3.irmp.ucl.ac.be/projects/delphes/wiki/WorkBook/Pythia8


== 0) Download data sample ==

From the Delphes main directory, download the pseudo-data file {{{data.root}}} :
{{{
wget -O data.root https://cernbox.cern.ch/index.php/s/MIjpBmZmXjHmNm2/download
}}}

== 1) Access information in the ROOT file

Open the ROOT file (ignore the various warnings) and visualize its content via the TBrowser.

{{{
root -l data.root
TBrowser t;
}}} 

Open the Delphes Tree and then Event branch (by double-clicking). How many events are in this dataset?
Likewise explore the Tree content by looking at other branches. 

Non trivial information can also be plotted from the command line. 

{{{
TFile *f = TFile::Open("data.root");
TBrowser browser;
f->Get("Delphes")->Draw("Muon.PT");
}}}

Some information is not directly accessible from the browser interface but can nevertheless be access via command line. Every Delphes object has a 4-vector (called P4()) member of type TLorentzVector (see here for documentation: https://root.cern.ch/doc/master/classTLorentzVector.html)

So for example to plot the magnitude of the 3-momenta of the jets, or the jet invariant mass:

In order to perform an event-loop with a more sophisticated event selection you will have to write a small python macro. This macro takes as input the {{{data.root}}} and produce an output file that contains a couple of histograms. Download the file called {{{example.py}}} (see the "attachment" link below), and run it via the command:

{{{
python example.py data.root hist_data.root
}}}

This will create a file called {{{hist_data.root}}} that can be browsed as done earlier with {{{data.root}}}. 


== 2) Characterisation of a BSM excess

a) The provided data corresponds to LHC events that contains at least two isolated leptons (lepton=electron/muon) with pT > 15. Prove it!

b) The data contains the equivalent of an integrated luminosity of 60 pb-1. It contains an excess corresponding to a new physical state. Find the observable that makes this excess explicit. 

c) What is the new particle's mass? 

d) What does it decay into?

e) For each decay channel define an event selection (the signal region) that maximises the significance of observation of this new state.  

f) For each decay channel, express the cross section times branching ratio as a function of the number of observed events (Nobs) and the number of background events (Nb) in the signal region, the integrated luminosity (L) and the event selection efficiency. 

g) How would you measure the background yield (Nb) from this data sample? 

h) How would you measure the signal efficiency?

i) Compute the cross-section of this process with the available data.  

j) Produce a stacked plot of data signal and background in all the decay channels