From 126d0ed84026667d4139b3b6de8dbc6776c707ce Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Renata=20Kopecn=C3=A1?= <kopecna@physi.uni-heidelberg.de>
Date: Tue, 25 Jan 2022 17:58:07 +0100
Subject: [PATCH] Update 'Selection code'

---
 Selection-code.md | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/Selection-code.md b/Selection-code.md
index 6514ba5..e7881ad 100644
--- a/Selection-code.md
+++ b/Selection-code.md
@@ -38,7 +38,7 @@ 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](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 via [[nTrackWeights.cpp][nTrackWeights]]
+Now the peaking background is removed, we can proceed to reweighting via [[nTrackWeights.cpp|nTrackWeights]]
 ```
 .L nTrackWeights.cpp+
 WeightAll(true,1,true); ReweightReferenceMC(true,1,true); ReweightPHSPMC(true,1,true);
@@ -51,13 +51,13 @@ Check the MVA variables are agreeing after weighting them with sWeights
 compareAll(1); compareAll(2);
 ```
 
-Reweighted Data and Monte Carlo can be used for the MVA training
+Reweighted Data and Monte Carlo can be used for the [[MVA.cpp|MVA training]]
  ```
 .L MVA.cpp+
  RunMVA(1); RunMVA(2);
 ```
 
-Apply the MVA to all the MC and Data 
+Apply the MVA to all the MC and Data using [[TMVA.cpp|TMVA Class application]]
  ```
 .L TMVAClassApp.cpp+
 TMVAClassAppAll(1); TMVAClassAppAll(2);
@@ -81,7 +81,7 @@ Check the variables again
 compareAll(1); compareAll(2);
 ```
 
-Run the MVA training, make nice plots, apply the MVA and remove multiple candidates
+Run the MVA training, [[make nice plots|Plot MVA]], apply the MVA and remove multiple candidates
 
 ```
 .L MVA.cpp+
@@ -104,13 +104,13 @@ Add variables to the MC samples **TODO**
 addAllVariablesAllMCSamples(1); addAllVariablesAllMCSamples(2);
 ```
 
-Get the eficiencies needed for the estimation  of the best MVA response cut
+Get the eficiencies needed for the estimation  of the best MVA response cut, defined in [[Efficiency.cpp|Efficiency.cpp]]
 ```
 .L Efficiency.cpp+
 runAllEff();
 ```
 
-Scan the significance in the MVA cut. Don't mind the 2012 and 2016 tags, they are just dummies
+Scan the significance in the MVA cut using the code in [[BDTcutScanner.cpp|BDTcutScanner]]. Don't mind the 2012 and 2016 tags, they are just dummies
 ```
 .L BDTcutScanner.cpp+
 ScanSignalAndBckgndEstimation("2012",1,0.01,false,false,false,true)
@@ -124,7 +124,7 @@ 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.
+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]].
  ```
 .L SignalStudy.cpp+
 plotYieldInQ2(true); plotYieldInQ2(false);