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lhcb-analysis-bs2dskpipi's Introduction

All code used for the analysis should go in this repository along with the latest version of the Ana-Note and the latest paper draft.

Feel free to add usefull information to this README :)

For instructions on how to compile and run the code see README in the corresponding directories.

Workflow:

  1. Minimize stripped data

in Selection/ :

source mini_data.sh

source mini_mc.sh

  1. For MC: Add corrected PID vars

in PIDCalib/ :

source runPIDCorr_signal.sh

source runPIDCorr_norm.sh

  1. Preselection

in Selection/ :

source select_data.sh

source select_mc.sh

  1. Fit normalization channel

in TD-MINT2/src/Users/dargent/MassFits :

./massFit < fitPreselectedNorm.txt

  1. Reweight MC

in TD-MINT2/src/Users/dargent/ DataVsMC :

./dataVsMC < dataVsMC.txt

  1. Train and apply BDT

in Selection:

Run in root:

TMVAClassification("BDTG")

TMVAClassificationApplication("Signal/Norm","Data/MC")

  1. Optimize BDT cut

in TD-MINT2/src/Users/dargent/MassFits :

./massFit < fitSignalForBDT.txt

  1. Fit final sample

in TD-MINT2/src/Users/dargent/MassFits :

./massFit < massFit.txt

Data samples:

Stripping output after loose preselection is applied and unnecessary branches are removed:

/auto/data/dargent/BsDsKpipi/Mini/Data(MC)/signal(norm)_Ds2KKpi(Ds2pipipi)_11(12/15/16).root

After preselection with BDT variables added:

/auto/data/dargent/BsDsKpipi/Preselected/Data(MC)/signal(norm)_Ds2KKpi(Ds2pipipi)_11(12/15/16).root

With BDT response:

/auto/data/dargent/BsDsKpipi/BDT/Data(MC)/signal(norm).root

Final sample:

/auto/data/dargent/BsDsKpipi/Final/Data(MC)/signal(norm).root

Final sample in MINT format:

/auto/data/dargent/BsDsKpipi/Mint/Data(MC)/signal(norm).root

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