diff --git a/Selection-code.md b/Selection-code.md
index 503ed10..8a793e1 100644
--- a/Selection-code.md
+++ b/Selection-code.md
@@ -31,7 +31,7 @@ The code consists of several C++ scripts that are compiled and executed in ROOT.
 We used ROOT 6.06.02. 
 
 
-First, compile and run the preselection. It is defined in [[BDTSelection.cpp|BDTSelection]].
+First, compile and run the preselection. It is defined in [[BDTSelection.cpp|BDTSelection]]. This reads the files with **stripped** data and creates new tuples with **preselected** data.
 ```
 .L BDTSelection.cpp+
 runAllSignalData(1); runAllSignalData(2);
@@ -45,7 +45,7 @@ Then, run a python script  [[Rescale_pi0momentum.py|Rescale pi0 momentum]] perfo
 lb-conda default python Scripts/Rescale_pi0momentum.py
 ```
 
-Next step is to compile and perform the MC Truth-Matching, saved in  [[MCtruthmatching.cpp|MCtruthmatching.cpp]]. The Truth-matching procedure is in detail described in [my thesis](http://www.physi.uni-heidelberg.de/Publications/thesis_Kopecna_final.pdf).
+Next step is to compile and perform the MC Truth-Matching, saved in  [[MCtruthmatching.cpp|MCtruthmatching.cpp]]. The Truth-matching procedure is in detail described in [my thesis](http://www.physi.uni-heidelberg.de/Publications/thesis_Kopecna_final.pdf). 
 ```
 .L MCtruthmatching.cpp+
 TruthMatchAllAll(1); TruthMatchAllAll(2);
@@ -60,7 +60,7 @@ addAllXMuMuMass(true,true,2); addAllXMuMuMass(false,true,2); applyAllVetoKplusMu
 
 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|B-mass-model]].
 
-Now the peaking background is removed, we can proceed to reweighting via [[nTrackWeights.cpp|nTrackWeights]]
+Now the peaking background is removed, we can proceed to reweighting via [[nTrackWeights.cpp|nTrackWeights]]. It takes the **preselected** tuples and create new **weighted** ones, with the tag BDT input.
 ```
 .L nTrackWeights.cpp+
 WeightAll(true,1,true); ReweightReferenceMC(true,1,true); ReweightPHSPMC(true,1,true);
@@ -79,7 +79,7 @@ Reweighted Data and Monte Carlo can be used for the [[MVA.cpp|MVA-Class]]
  RunMVA(1); RunMVA(2);
 ```
 
-Apply the MVA to all the MC and Data using [[TMVAClassApp.cpp|TMVA Class application]]
+Apply the MVA to all the MC and Data using [[TMVAClassApp.cpp|TMVA Class application]]. This also creates new tuples with the tag BDT output.
  ```
 .L TMVAClassApp.cpp+
 TMVAClassAppAll(1); TMVAClassAppAll(2);
@@ -146,7 +146,7 @@ Make a nice TGraph from the scan; when creating the scan, it can happen that eg
 python ReorganizeTGraph.py
 ``` 
 
-Use the MVA scan to plot the signal yields, apply the MVA cut and compare the yields to the CMS results (see [[SignalStudy.cpp|Signal Study]]).
+Use the MVA scan to plot the signal yields, apply the MVA cut and compare the yields to the CMS results (see [[SignalStudy.cpp|Signal Study]]). It also creates the tuples used by the [[FCNC fitter|FCNC fitter]] tagged as BDT output selection.
  ```
 .L SignalStudy.cpp+
 plotYieldInQ2(true); plotYieldInQ2(false);