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Analysis of Higgs boson decays to four leptons using data and simulation from the CMS detector

This repository contains an analysis using reduced NanoAOD files created from CMS Open Data studying the decay of a Higgs boson into four leptons. The code produces plots such as shown below which can be used to study the physics in the CMS datasets. You can find a more extensive description of the analysis in the record on the CERN Open Data portal.

Note that the analysis is not validated and should be used only for educational purposes, in particular since no systematic uncertainties are included.

How to run this?

Get the software stack

The analysis needs solely a ROOT installation (6.22 or greater). You can get the software easily using the CMS Open Data VM and CVMFS. Just run the following command in the terminal (adapted to your system) to source an appropriate software stack:

source /cvmfs/sft.cern.ch/lcg/views/LCG_99/x86_64-centos7-gcc10-opt/setup.sh

Skim the datasets

The skimming reduces the inital dataset to only the events needed for the analysis. This part is written in C++ in the file skim.cxx. To compile and run the program, use the following commands.

g++ -g -O3 -Wall -Wextra -Wpedantic -o skim skim.cxx $(root-config --cflags --libs)
./skim

Produce histograms

Next, we want to produce histograms for most of the variables in the dataset. To make a comparison from simulation to data, we have to produce a histogram for the simulated processes and the actual data events recorded at CMS. The histograms are produced in a Python script implemented in histograms.py and can be run with following command.

python histograms.py

Make plots

The last step of this analysis is the combination of the previously produced histograms to figures showing the simulated events and the data recorded at CMS on top of each other. This allows us to draw conclusions about the agreement between simulation and data and gives insights into the recorded data regarding the composition of the physical processes and their properties. Run the following Python script to make plots from the output of the previous step.

python plot.py