FCCAnalyses

This package produce flat ROOT trees using FCCSW EDM root files produced with the EventProducer. As usual, if you aim at contributing to the repository, please fork it, develop and submit pull requests. To have access to the FCC samples, you need to subscribe to one of the following e-group (with owner approval) fcc-eos-read-xx with xx=ee,hh,eh. For the time being, the configuration files are accessible on helsens public afs. This is not optimal and will be changed in the future, thus you are also kindly asked to contact [email protected] and request access to /afs/cern.ch/work/h/helsens/public/FCCDicts/.

Analysers documentation here

Table of contents

• FCCAnalyses
• Table of contents
• RootDataFrame based
  • Getting Started
  • Generalities
  • Pre selection
  • Final selection
  • Plotting

RootDataFrame based

Using ROOT dataframe allows a much quicker processing time. In this implementation, everything from reading EDM4Hep or FCCSW EDM files on eos and producing flat n-tuples, to running a final selection and plotting the results will be explained. ROOT dataframe documentation is availabe here

Getting Started

In order to use ROOT dataframe for the analyses, the dictionary with the analyzers needs to be built and put into LD_LIBRARY_PATH (this happens in setup.sh)

First check if FCCSW is setup. If the command which fccrun returns something like: /cvmfs/sw.hsf.org/spackages/linux-centos7-broadwell/gcc-8.3.0/fccsw-develop-q57ahua7lm65fvxnzekozih4mgvzptlx/scripts/fccrun then you are good to go, if not please run:

source /cvmfs/fcc.cern.ch/sw/latest/setup.sh

source ./setup.sh
mkdir build install
cd build
cmake .. -DCMAKE_INSTALL_PREFIX=../install
make install

Each time changes are made in analyzers/dataframe/ please re-compile!

Generalities

Each analysis is hosted in a single directory, for example examples/FCCee/higgs/mH-recoil/mumu/ and contains the same kind of files, please use the same naming convention for all analysis.

1. analysis.py: This class that is used to define the list of analysers and filters to run on as well as the output variables.
2. preSel.py: This configuration file is used to define how to run the analysis.py. It contains the list of samples, the number of CPUs, the fraction of the original sample to process and the base directory for the yaml files (that contains the informations about the samples). This will run the analysis.py with a common code config/runDataFrame.py (this last file is common to all analyses and should not be touched).
3. finalSel.py: This configuration file contains the final selections and it runs over the locally produced flat ntuples from the preSel.py. It contains a link to the procDict.json for getting cross section etc...(this might be removed later to include everything in the yaml, closer to the sample), the list of processes, the number of CPU, the cut list, and the variables (that will be both written in a TTree and in the form of TH1 properly normalised to an integrated luminosity of 1pb-1.
4. plots.py: This configuration files is used to select the final selections from running finalSel.py to plot. Informations about how to merge processes, write some extra text, normalise to a given integrated luminosity etc... For the moment it is possible to only plot one signal at the time, but several backgrounds.

Pre-selection

The pre-selection runs over already existing and properly registered FCCSW EDM events. The dataset names with the corresponding statistics can be found here. One important parameter is the fraction of the total dataset to run. It can be found in the preSel.py file by setting a value between ]0,1]. For example fraction=0.1 will run over 10% of the statistics. Reading the files on eos, and with 15 CPUs we observe processing speeds between 3000 and 10000 events per seconds depending on the number of files. Only run full statistics after having done all the proper testing and analysis design as it can take some time (that of course depends on the sample total statistics). To run the pre-selection of the ZH_Zmumu analysis, just run:

python examples/FCCee/higgs/mH-recoil/mumu/preSel.py

This will output 3 files in outputs/FCCee/higgs/mH-recoil/mumu/ following the parameter outdir in the preSel.py configuration file.

Final selection

The final selection runs on the pre-selection files that we produced in the Pre-selection step. In the configuration file finalSel.py we define the various cuts to run on and the final variables to be stored in both a TTree and histograms. This is why the variables needs extra fields like title, number of bins and range for the histogram creation. In this example it should run like:

python examples/FCCee/higgs/mH-recoil/mumu/finalSel.py

This will create 2 files per selection SAMPLENAME_SELECTIONNAME.root for the TTree and SAMPLENAME_SELECTIONNAME_histo.root for the histograms. SAMPLENAME and SELECTIONNAME corresponds to the name of the sample and selection respectively in the configuration file.

Plotting

The plotting configuration file plots.py contains informations about plotting details for plots rendering but also ways of combining samples for plotting. In this example just run like:

doPlots.py examples/FCCee/higgs/mH-recoil/mumu/plots.py

This will produce the plots in the outdir defined in the configuration file.