jmhogan / mtopfromebpeak

Extraction of the top-quark mass by measuring the peak position of the energy of b-tagged jets in the laboratory frame

MtopFromEbPeak

This repository contains some skeletons for the long exercise related to Top physics at the CMS Data Analysis School 2018 where you will find the most recent version of the instructions below. The objective is not only to extract the top-quark mass by measuring the peak position of the energy of b-tagged jets in the laboratory frame, but also to get more familiar with high-energy physics analyses, making use of Python, C and Root.

Installation

Before installing a fresh CMSSW release, do

• in csh/tcsh:

source /cvmfs/cms.cern.ch/cmsset_default.csh
setenv SCRAM_ARCH slc6_amd64_gcc530

• in bash/sh:

source /cvmfs/cms.cern.ch/cmsset_default.sh
export SCRAM_ARCH=slc6_amd64_gcc530

Then, install the code by doing

cmsrel CMSSW_9_2_14
cd CMSSW_9_2_14/src
cmsenv
scram b
git clone [email protected]:CMSDASAtLPC/LongExerciseTopMass.git
cd LongExerciseTopMass/

This repository is organised in 4 parts:

• in the familiarization folder, you will write a Python script to read a TTree from a TFile;
• in the analyzeNplot folder, you will write a Python script to select top-pair events that decay in the eμ channel, compare the selection on data and simulation (control distributions and event yields), and propagate the sources of systematic uncertainties to the b jet energy peak;
• in the fitNcalibrate folder, you will write a python script to fit the b jet energy peak. In this same folder, you will then calibrate the b jet energy peak measured for the set of selection criteria previously defined to the expected b jet energy peak, from which the top-quark mass can be easily extracted. You will also evaluate the performances of the method.
• in the finalize folder, a macro named showComparison.C will enable you to compare your result with the standard top-quark mass measurements performed with 8 TeV data.

Browsing the content of a TFile

The first step of the exercise consists in opening an input file stored in /eos/uscms/store/user/cmsdas/2018/long_exercises/TopMass/ and plotting the jet transverse momentum. Once you have written your own small Python script, you can compare it to the one named controlPlots.pyin the familiarization folder.

The input files have been produced thanks to a setup kindly provided by P. Silva.

Selecting events

This step must be run in the analyzeNplot folder.

To run the event selection and basic filling of histograms using a pre-defined list of samples and cross sections, one can use the following script

python runBJetEnergyPeak.py -i /store/user/cmsdas/2018/long_exercises/TopMass -j data/samples_Run2016_25ns.json -o nominal -n 8

Indeed, the Root files to analyze are stored in /eos/uscms/store/user/cmsdas/2018/long_exercises/TopMass/, while the data subfolder contains information for reweighting (cross-sections, PU, b-tagging....). This steps takes approximatively 10-15 mn.

The results are stored in Root files int the nominal subfolder. They can be plotted together and compared to data using

python plotter.py -i nominal -j data/samples_Run2016_25ns.json  -l 35867.

Under nominal/plots you'll find a file called plotter.root, containing the histograms with the distributions normalized by integrated luminosity (35867 /pb,) together with png and pdf versions of the plots.

The number of events selected from data and simulations can be obtained from

python getNumberOfEvents.py -i nominal -o table -j data/samples_Run2016_25ns.json

Fitting the b jet energy peak

This step must be run in the fitNcalibrate folder. A python skeleton named fitPeak.py is provided. For MC, the usage is:

python fitPeak.py -i "nominal" -j "../analyzeNplot/data/samples_Run2016_25ns.json" -l 35867.

while for data, it is:

python fitPeak.py -i "nominal" -l 35867. -d

The argument following -i is the folder in which the plotter.root file has been previously produced. As previously, -l precedes the luminosity and -j the path for the json file.

For simulations, signal+background, normalized at their cross-sections, are fitted together.

Comparing your result with CMS legacy results

Once you have unblinded your analysis, you can do:

root -l
.L showComparison.C
showComparison(lumi, value, stat, syst)
.q

replacing lumi by the top-quark mass you measured, and stat and syst respectively by the statistical and systematic uncertainties of this measurement. You will get a comparison between your measurement and the standard top-quark mass measurements performed with 8 TeV data.