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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}}}, 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

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

- 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. 

- What is the new particle's mass? 

- What does it decay into?

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

- 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. 

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

- How would you measure the signal efficiency?

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

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