Update 'Selection code'

Selection-code.md

@@ -1,46 +1,83 @@
-The selection code is a set of C++ scripts. 
+The selection code is a set of C++ scripts. First, the running of the code is introduced. Then, each part of the code is explained.
 
 # Running the code
-When re-running everything do the following 
+When re-running everything, do the following
 
+First, compile and run the preselection
+```
  .L BDTSelection.cpp+
  runAllSignalData(1); runAllSignalData(2);
  runAllSignalMC(1); runAllSignalMC(2);
  runAllRefMC(1);  runAllRefMC(2);
  runAllPHSPMC(1); runAllPHSPMC(2);
+```
 
+Then,run a python script performing the Kstar MacGyver DTF
+```
  lb-conda default python Rescale_pi0momentum.py
+```
 
+Next step is to compile and perform the MC Truth-Matching
+```
  .L MCtruthmatching.cpp+
-
  TruthMatchAllAll(1); TruthMatchAllAll(2);
+```
 
+Then, we need to add the XMuMu mass variable and apply the KplusMuMu veto
+ ```
 .L CodeForTests/AddVariable.cpp+
  addAllXMuMuMass(true,true,1); addAllXMuMuMass(false,true,1); applyAllVetoKplusMuMuMass(1);
  addAllXMuMuMass(true,true,2); addAllXMuMuMass(false,true,2); applyAllVetoKplusMuMuMass(2);
+```
 
+We have all the preselection finished. Now we will need to fit the reconstructed B mass peak. For the instructions how to compile the code and make RooFit use double-sided Crystal Ball or ExpGauss, see [B mass model section](https://git.physi.uni-heidelberg.de/kopecna/EWP-BplusToKstMuMu-AngAna/wiki/B-mass-model).
+
+Now the peaking background is removed, we can proceed to reweighting
+```
  .L nTrackWeights.cpp+
  WeightAll(true,1,true); ReweightReferenceMC(true,1,true); ReweightPHSPMC(true,1,true);
  WeightAll(true,2,true); ReweightReferenceMC(true,2,true); ReweightPHSPMC(true,2,true);
+```
 
-  *** now compare all the variables ***
+Check the MVA variables are agreeing after weighting them with sWeights
-
+```
- .L MVA.cpp+
- RunMVA(1); RunMVA(2);
-
- .L TMVAClassApp.cpp+
- TMVAClassAppAll(1); TMVAClassAppAll(2);
-
- python RemoveMultipleCandidates.py -all
-
-  *** Now rerunning the weights as they are fixed to after-mva ***
- .L nTrackWeights.cpp+
- WeightAll(true,1,true); ReweightReferenceMC(true,1,true); ReweightPHSPMC(true,1,true);
- WeightAll(true,2,true); ReweightReferenceMC(true,2,true); ReweightPHSPMC(true,2,true);
-
  .L CodeForTests/compareVariables.cc+
  compareAll(1); compareAll(2);
+```
 
+Reweighted Data and Monte Carlo can be used for the MVA training
+ ```
+.L MVA.cpp+
+ RunMVA(1); RunMVA(2);
+```
+
+Apply the MVA to all the MC and Data 
+ ```
+.L TMVAClassApp.cpp+
+ TMVAClassAppAll(1); TMVAClassAppAll(2);
+```
+
+Remove all multiple candidates
+```
+python RemoveMultipleCandidates.py -all
+```
+
+We have to rerun the weights and therefore also the MVA: the shape of the B mass peak is fixed to the one after MVA. 
+```
+.L nTrackWeights.cpp+
+ WeightAll(true,1,true); ReweightReferenceMC(true,1,true); ReweightPHSPMC(true,1,true);
+ WeightAll(true,2,true); ReweightReferenceMC(true,2,true); ReweightPHSPMC(true,2,true);
+```
+
+Check the variables again
+```
+.L CodeForTests/compareVariables.cc+
+compareAll(1); compareAll(2);
+```
+
+Run the MVA training, make nice plots, apply the MVA and remove multiple candidates
+
+```
 .L MVA.cpp+
 RunMVA(1); RunMVA(2);
 
@@ -53,61 +90,93 @@ When re-running everything do the following
 TMVAClassAppAll(1); TMVAClassAppAll(2);
 
 python RemoveMultipleCandidates.py -all
+```
 
+Add variables to the MC samples **TODO**
+```
  .L CodeForTests/AddVariable.cpp+
  addAllVariablesAllMCSamples(1); addAllVariablesAllMCSamples(2);
+```
 
+Get the eficiencies needed for the estimation  of the best MVA response cut
+```
  .L Efficiency.cpp+
  runAllEff();
+```
 
+Scan the significance in the MVA cut. Don't mind the 2012 and 2016 tags, they are just dummies
+```
 .L BDTcutScanner.cpp+
 ScanSignalAndBckgndEstimation("2012",1,0.01,false,false,false,true)
 ScanSignalAndBckgndEstimation("2016",2,0.01,false,false,false,true)
 getMaxBDTresponse("2012",1,true,true,0,false,false)
 getMaxBDTresponse("2016",2,true,false,0,false,false)
+```
 
-
+Make a nice TGraph from the scan; when creating the scan, it can happen that eg an estimation at cut at  0.95 happens before a cut at 0.92. This script just takes it and makes a pretty clean plot.
+```
 python ReorganizeTGraph.py
+``` 
 
+Use the MVA scan to plot the signal yields, apply the MVA cut and compare the yields to the CMS results.
+ ```
 .L SignalStudy.cpp+
  plotYieldInQ2(true); plotYieldInQ2(false);
  ApplyCutPerYearAll(1);  ApplyCutPerYearAll(2);
  printYileds(false); printYileds(true)
  yieldComparison(1,getTMVAcut(1));
  yieldComparison(2,getTMVAcut(2));
+```
 
 
+## Mass Fit compilation
  Recompile mass fit
 
+```
  .L BmassShape/SignalType.cpp+
  .L BmassShape/SignalPdf.cpp+
  .L BmassShape/BackgroundType.cpp+
  .L BmassShape/BackgroundPdf.cpp+
  .L BmassShape/ParamValues.cpp+
  .L MassFit.cpp+
+```
  
- 
+ ## Checking the inclusive sample
- Check the inclusive sample
+```
  .L BDTSelection.cpp+
  runAllIncMC(1); runAllIncMC(2)
+```
 
  lb-conda default python Rescale_pi0momentum.py (CAREFUL, NEEDS TO BE SET BY HAND)
 
+```
  .L MCtruthmatching.cpp+
  TruthMatchAllBkg(true,1,false,false,true); TruthMatchAllBkg(true,2,false,false,true);
+```
 
+```
  .L CodeForTests/AddVariable.cpp+ (CAREFUL, NEEDS TO BE SET BY HAND)
  addAllXMuMuMass(true,true,1,true,true,false); addAllXMuMuMass(false,true,1,true,true,false); applyAllVetoKplusMuMuMass(1,true,true,false);
  addAllXMuMuMass(true,true,2,true,true,false); addAllXMuMuMass(false,true,2,true,true,false); applyAllVetoKplusMuMuMass(2,true,true,false);
-
+```
+```
 lb-conda default python Rescale_pi0momentum.py (CAREFUL, NEEDS TO BE SET BY HAND)
+```
 
+
+```
  .L TMVAClassApp.cpp+
  TMVAClassAppInc(1); TMVAClassAppInc(2);
+```
 
+```
  python RemoveMultipleCandidates.py -all (CAREFUL, NEEDS TO BE SET BY HAND)
+```
 
+
+```
  .L CodeForTests/InclusiveCheck.cpp+
  plotTM(1,true); plotTM(2,true)
  plotTM(1,false); plotTM(2,false)
+```