add trig obj

_episodes/04-HLT_efficiency.md

@@ -92,6 +92,7 @@ We will first run this exercise on MiniAOD format, then run it again on NanoAOD.
 > scram b -j 4
 > cd $CMSSW_BASE/src/ShortExerciseTrigger2023/ShortExerciseTrigger/test
 > ~~~
+> {: .language-bash}
 > Copy the skimmed NanoAOD file to your working directory as follows:
 > ~~~
 > xrdcp root://cmseos.fnal.gov//store/user/cmsdas/2023/short_exercises/Trigger/New_NanoAOD_M1000.root .

_episodes/05-TriggerObj.md

@@ -9,6 +9,56 @@ objectives:
 ---
 
 > ## MiniAOD
+> In the [efficiency measurement Ex.][lesson-04-HLT_efficiency], we performed a simple efficiency measurement using the `TriggerResults` product in a MiniAOD file and a NanoAOD file.<br>
+> Sometimes, we want to know what is the exact object reconstructed and used in the HLT path.<br>
+> This is what `TriggerObjectStandAloneCollection` contains - the actual physics objects reconstructed at the HLT.
+> 
+> Have a look at the code in `plugins/SingleMuTrigAnalyzerMiniAOD.cc`, especially the analyze function starting from line 95, as well as the configuration file `ana_SingleMuMiniAOD.py`.
+> 
+> > ## Additional info
+> > The configuration file shows that also this time we are using as input a skimmed MiniAOD file called `skim_dimu20_SingleMuon_2016G_ReReco_180k.root`. In case you are wondering again, this skim has been produced with the configuration `skim_dimu20.py`, requires two offline muons with pt above 20 GeV.
+> {: .objectives}
+> 
+> This time we don't have a signal trigger and a separate reference trigger. Instead, we focus only on one single-muon trigger, namely HLT_IsoMu24:
+> ~~~
+> process.singleMuTrigAnalyzerMiniAOD.triggerName = cms.untracked.string("HLT_IsoMu24_v2")
+> ~~~
+> {: .language-python}
+> The `SingleMuTrigAnalyzerMiniAOD.cc` analyzer is longer and more complicated than the one in [efficiency measurement Ex.][lesson-04-HLT_efficiency], and we will discuss it not only in this exercise, but also in two others that follow. So don't worry if some parts look somewhat mysterious first.
+> 
+> Similarly to [efficiency measurement Ex.][lesson-04-HLT_efficiency], in line 129 the name of the HLT path (`triggerName_`) is used to retrieve the corresponding index (`triggerIndex`):
+> ~~~
+> const unsigned int triggerIndex(hltConfig_.triggerIndex(triggerName_));
+> ~~~
+> {: .language-cpp}
+> which is then used (in line 148) to access the HLT decision to accept or reject the event:
+> ~~~
+>   bool accept = triggerResultsHandle_->accept(triggerIndex);
+> ~~~
+> {: .language-cpp}
+> You can find some new, more interesting tricks on lines 180-190. 
+> There we create an empty vector `trigMuons`, loop over all trigger objects (that is, physics objects reconstructed at HLT), select the objects that HLT classified as muons that would pass our single-muon trigger, and add the four-vectors of these trigger-level muon objects to the trigMuons vector:
+> ~~~
+>   std::vector trigMuons;
+>   if (verbose_) cout << "found trigger muons:" << endl;
+>   const edm::TriggerNames &names = iEvent.triggerNames(*triggerResultsHandle_);
+>   for (pat::TriggerObjectStandAlone obj : *triggerOSA_) {
+>     obj.unpackPathNames(names);
+>     if ( !obj.id(83) ) continue; // muon type id
+>     if ( !obj.hasPathName( triggerName_, true, true ) ) continue; // checks if object is associated to last filter (true) and L3 filter (true)
+>     trigMuons.push_back(LorentzVector(obj.p4()));
+>     if (verbose_) cout << "  - pt: " << obj.pt() << ", eta: " << obj.eta() << ", phi: " << obj.phi() << endl;
+>   } // loop on trigger objects
+> ~~~
+> {: .language-cpp}
+> 
+> Run the configuration file as follows:
+> ~~~
+> cd $CMSSW_BASE/src/ShortExerciseTrigger2023/ShortExerciseTrigger/test
+> cmsRun ana_SingleMuMiniAOD.py 
+> ~~~
+> {: .language-bash}
+> The code will output a file called `histos_SingleMuTrigAnalyzer.root`, which we will inspect in the next exercise.
 {: .solution}
 
 > ## NanoAOD