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Update 'Selection code'

Renata Kopecná 2022-02-07 08:53:41 +01:00
parent 2a8398c2a9
commit 8997566e9c
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Selection-code.md

@ -10,10 +10,10 @@ The selection code is a set of C++ scripts. First, the running of the code is in
## Setting up ROOT ## Setting up ROOT
**TODO** **TODO**
In order to make pretty plots, dedicated code in [[Design.cpp|Design.cpp]] is always checked for compilation when starting ROOT. This is done by creating a `.rootrc` file in your home directory and telling ROOT to always compile the Design.cpp: In order to make pretty plots, dedicated code in [[Design.cpp|Design.cpp]] is always checked for compilation when starting ROOT. This is done by creating a `.rootalias.C` file in your home directory and telling ROOT to always compile the Design.cpp:
``` ```
{ {
gROOT->ProcessLine(".L path/to/your/folder/Design.cpp+"); gROOT->ProcessLine(".L path/to/your/folder/Code/Design.cpp+");
} }
``` ```
@ -55,18 +55,20 @@ There are 5 levels of verbosity: The level of verbosity is defined in [GlobalFun
# Running the code # Running the code
The code consists of several C++ scripts that are compiled and executed in ROOT. Before every step, it is worth closing and opening ROOT, as it is possible ROOT will complain about redefinition of functions. The code consists of several C++ scripts that are compiled and executed in ROOT. **Before every step, it is worth closing and opening ROOT**, as it is possible ROOT will complain about redefinition of functions.
We used ROOT 6.06.02. We used ROOT 6.06.02.
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. 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.
``` ```
root
.L BDTSelection.cpp+ .L BDTSelection.cpp+
runAllSignalData(1); runAllSignalData(2); runAllSignalData(1); runAllSignalData(2);
runAllSignalMC(1); runAllSignalMC(2); runAllSignalMC(1); runAllSignalMC(2);
runAllRefMC(1); runAllRefMC(2); runAllRefMC(1); runAllRefMC(2);
runAllPHSPMC(1); runAllPHSPMC(2); runAllPHSPMC(1); runAllPHSPMC(2);
.q
``` ```
Then, run a python script [[Rescale_pi0momentum.py|Rescale-pi0-momentum]] performing the Kstar MacGyver DTF Then, run a python script [[Rescale_pi0momentum.py|Rescale-pi0-momentum]] performing the Kstar MacGyver DTF
@ -76,42 +78,54 @@ 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).
``` ```
root
.L MCtruthmatching.cpp+ .L MCtruthmatching.cpp+
TruthMatchAllAll(1); TruthMatchAllAll(2); TruthMatchAllAll(1); TruthMatchAllAll(2);
.q
``` ```
Then, we need to add the XMuMu mass variable and apply the KplusMuMu veto ([[AddVariable.cpp|AddVariable.cpp]]) Then, we need to add the XMuMu mass variable and apply the KplusMuMu veto ([[AddVariable.cpp|AddVariable.cpp]])
``` ```
root
.L AddVariable.cpp+ .L AddVariable.cpp+
addAllXMuMuMass(true,true,1); addAllXMuMuMass(false,true,1); applyAllVetoKplusMuMuMass(1); addAllXMuMuMass(true,true,1); addAllXMuMuMass(false,true,1); applyAllVetoKplusMuMuMass(1);
addAllXMuMuMass(true,true,2); addAllXMuMuMass(false,true,2); applyAllVetoKplusMuMuMass(2); addAllXMuMuMass(true,true,2); addAllXMuMuMass(false,true,2); applyAllVetoKplusMuMuMass(2);
.q
``` ```
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]]. 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.cpp]]. It takes the **preselected** tuples and create new **weighted** ones, with the tag BDT input. Now the peaking background is removed, we can proceed to reweighting via [[nTrackWeights.cpp|nTrackWeights.cpp]]. It takes the **preselected** tuples and create new **weighted** ones, with the tag BDT input.
``` ```
root
.L nTrackWeights.cpp+ .L nTrackWeights.cpp+
WeightAll(true,1,true); ReweightReferenceMC(true,1,true); ReweightPHSPMC(true,1,true); WeightAll(true,1,true); ReweightReferenceMC(true,1,true); ReweightPHSPMC(true,1,true);
WeightAll(true,2,true); ReweightReferenceMC(true,2,true); ReweightPHSPMC(true,2,true); WeightAll(true,2,true); ReweightReferenceMC(true,2,true); ReweightPHSPMC(true,2,true);
.q
``` ```
Check the MVA variables are agreeing after weighting them with sWeights [[CompareVariables.cpp|CompareVariables.cpp]]. (Yes, there is a [[dedicated tool|Comparison-tool]], but this was intially working, and working well, so it was kept for checking the variables used in the MVA training.) **TODO** Check the MVA variables are agreeing after weighting them with sWeights [[CompareVariables.cpp|CompareVariables.cpp]]. (Yes, there is a [[dedicated tool|Comparison-tool]], but this was intially working, and working well, so it was kept for checking the variables used in the MVA training.) **TODO**
``` ```
root
.L Scripts/compareVariables.cc+ .L Scripts/compareVariables.cc+
compareAll(1); compareAll(2); compareAll(1); compareAll(2);
.q
``` ```
Reweighted Data and Monte Carlo can be used for the [[MVA.cpp|MVA-Class]] Reweighted Data and Monte Carlo can be used for the [[MVA.cpp|MVA-Class]]
``` ```
root
.L MVA.cpp+ .L MVA.cpp+
RunMVA(1); RunMVA(2); RunMVA(1); RunMVA(2);
.q
``` ```
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. 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.
``` ```
root
.L TMVAClassApp.cpp+ .L TMVAClassApp.cpp+
TMVAClassAppAll(1); TMVAClassAppAll(2); TMVAClassAppAll(1); TMVAClassAppAll(2);
.q
``` ```
Remove all multiple candidates, defined in [[RemoveMultipleCandidates.py|RemoveMultipleCandidates.py]] Remove all multiple candidates, defined in [[RemoveMultipleCandidates.py|RemoveMultipleCandidates.py]]
@ -121,20 +135,25 @@ 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. 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.
``` ```
root
.L nTrackWeights.cpp+ .L nTrackWeights.cpp+
WeightAll(true,1,true); ReweightReferenceMC(true,1,true); ReweightPHSPMC(true,1,true); WeightAll(true,1,true); ReweightReferenceMC(true,1,true); ReweightPHSPMC(true,1,true);
WeightAll(true,2,true); ReweightReferenceMC(true,2,true); ReweightPHSPMC(true,2,true); WeightAll(true,2,true); ReweightReferenceMC(true,2,true); ReweightPHSPMC(true,2,true);
.q
``` ```
Check the variables again Check the variables again
``` ```
root
.L Scripts/compareVariables.cc+ .L Scripts/compareVariables.cc+
compareAll(1); compareAll(2); compareAll(1); compareAll(2);
.q
``` ```
Run the MVA training, [[make nice plots|PlotTMVA.cpp]], apply the MVA and remove multiple candidates Run the MVA training, [[make nice plots|PlotTMVA.cpp]], apply the MVA and remove multiple candidates
``` ```
root
.L MVA.cpp+ .L MVA.cpp+
RunMVA(1); RunMVA(2); RunMVA(1); RunMVA(2);
@ -145,29 +164,36 @@ testFunction(1); testFunction(2)
.L TMVAClassApp.cpp+ .L TMVAClassApp.cpp+
TMVAClassAppAll(1); TMVAClassAppAll(2); TMVAClassAppAll(1); TMVAClassAppAll(2);
.q
python RemoveMultipleCandidates.py -all python RemoveMultipleCandidates.py -all
``` ```
[[Add variables|AddVariable.cpp]] to the MC samples [[Add variables|AddVariable.cpp]] to the MC samples
``` ```
root
.L AddVariable.cpp+ .L AddVariable.cpp+
addAllVariablesAllMCSamples(1); addAllVariablesAllMCSamples(2); addAllVariablesAllMCSamples(1); addAllVariablesAllMCSamples(2);
.q
``` ```
Get the eficiencies needed for the estimation of the best MVA response cut, defined in [[Efficiency.cpp|Efficiency.cpp]] Get the eficiencies needed for the estimation of the best MVA response cut, defined in [[Efficiency.cpp|Efficiency.cpp]]
``` ```
root
.L Efficiency.cpp+ .L Efficiency.cpp+
runAllEff(); runAllEff();
.q
``` ```
Scan the significance in the MVA cut using the code in [[BDTcutScanner.cpp|BDTcutScanner.cpp]]. 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.cpp]]. Don't mind the 2012 and 2016 tags, they are just dummies
``` ```
root
.L BDTcutScanner.cpp+ .L BDTcutScanner.cpp+
ScanSignalAndBckgndEstimation("2012",1,0.01,false,false,false,true) ScanSignalAndBckgndEstimation("2012",1,0.01,false,false,false,true)
ScanSignalAndBckgndEstimation("2016",2,0.01,false,false,false,true) ScanSignalAndBckgndEstimation("2016",2,0.01,false,false,false,true)
getMaxBDTresponse("2012",1,true,true,0,false,false) getMaxBDTresponse("2012",1,true,true,0,false,false)
getMaxBDTresponse("2016",2,true,false,0,false,false) getMaxBDTresponse("2016",2,true,false,0,false,false)
.q
``` ```
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|ReorganizeTGraph.py]] just takes it and makes a pretty clean plot. 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|ReorganizeTGraph.py]] just takes it and makes a pretty clean plot.
@ -177,33 +203,41 @@ 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]]). It also creates the tuples used by the [[FCNC fitter|FCNC fitter]] tagged as BDT output selection. 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.
``` ```
root
.L SignalStudy.cpp+ .L SignalStudy.cpp+
plotYieldInQ2(true); plotYieldInQ2(false); plotYieldInQ2(true); plotYieldInQ2(false);
ApplyCutPerYearAll(1); ApplyCutPerYearAll(2); ApplyCutPerYearAll(1); ApplyCutPerYearAll(2);
printYileds(false); printYileds(true) printYileds(false); printYileds(true)
yieldComparison(1,getTMVAcut(1)); yieldComparison(1,getTMVAcut(1));
yieldComparison(2,getTMVAcut(2)); yieldComparison(2,getTMVAcut(2));
.q
``` ```
## Checking the inclusive sample ## Checking the inclusive sample
``` ```
root
.L BDTSelection.cpp+ .L BDTSelection.cpp+
runAllIncMC(1); runAllIncMC(2) runAllIncMC(1); runAllIncMC(2)
.q
``` ```
lb-conda default python Rescale_pi0momentum.py (CAREFUL, NEEDS TO BE SET BY HAND) lb-conda default python Rescale_pi0momentum.py (**CAREFUL, NEEDS TO BE SET BY HAND**)
``` ```
root
.L MCtruthmatching.cpp+ .L MCtruthmatching.cpp+
TruthMatchAllBkg(true,1,false,false,true); TruthMatchAllBkg(true,2,false,false,true); TruthMatchAllBkg(true,1,false,false,true); TruthMatchAllBkg(true,2,false,false,true);
.q
``` ```
Then, we need to add the variables to the inclusive sample. This HAS TO BE SET BY HAND in [[AddVariable.cpp|AddVariable.cpp]] before compilation! Then, we need to add the variables to the inclusive sample. **This HAS TO BE SET BY HAND** in [[AddVariable.cpp|AddVariable.cpp]] before compilation!
``` ```
root
.L AddVariable.cpp+ .L AddVariable.cpp+
addAllXMuMuMass(true,true,1,true,true,false); addAllXMuMuMass(false,true,1,true,true,false); applyAllVetoKplusMuMuMass(1,true,true,false); 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); addAllXMuMuMass(true,true,2,true,true,false); addAllXMuMuMass(false,true,2,true,true,false); applyAllVetoKplusMuMuMass(2,true,true,false);
.q
``` ```
Similarly, before applying the MacGyver DTF, the paths in [[Rescale_pi0momentum.py|Rescale-pi0-momentum]] have to be set by hand! Similarly, before applying the MacGyver DTF, the paths in [[Rescale_pi0momentum.py|Rescale-pi0-momentum]] have to be set by hand!
``` ```
@ -211,8 +245,10 @@ lb-conda default python Rescale_pi0momentum.py
``` ```
``` ```
root
.L TMVAClassApp.cpp+ .L TMVAClassApp.cpp+
TMVAClassAppInc(1); TMVAClassAppInc(2); TMVAClassAppInc(1); TMVAClassAppInc(2);
.q
``` ```
Also the paths in [[RemoveMultipleCandidates.py|RemoveMultipleCandidates.py]] have to be set by hand! Also the paths in [[RemoveMultipleCandidates.py|RemoveMultipleCandidates.py]] have to be set by hand!
``` ```
@ -220,8 +256,10 @@ python RemoveMultipleCandidates.py -all
``` ```
Lastly, make the truth-matching plots using [[InclusiveCheck.cpp||InclusiveCheck.cpp]]. Lastly, make the truth-matching plots using [[InclusiveCheck.cpp||InclusiveCheck.cpp]].
``` ```
root
.L Scripts/InclusiveCheck.cpp+ .L Scripts/InclusiveCheck.cpp+
plotTM(1,true); plotTM(2,true) plotTM(1,true); plotTM(2,true)
plotTM(1,false); plotTM(2,false) plotTM(1,false); plotTM(2,false)
.q
``` ```