Measuring trigger efficiencies

Overview

Teaching: 30 min
Exercises: 0 min

Questions

Objectives

• Learn how to access the trigger information stored in MiniAOD and NanoAOD

• Learn what is trigger objects and how to access them

• Measure trigger efficiency using the tag-and-probe method

Prerequisites

Set up your machine following instructions in setup first.

Objective

The goal of the following exercises is to learn how to access and play with the trigger objects in our data and compute the efficiency of a specific HLT path and look also at its Level 1 (L1) seed.

The focus will be on a HLT path used during the 2016 data-taking to select events with a certain amount of missing transverse energy: HLT_PFMET170_HBHECleaned_v*.

Compute a MET trigger efficiency

We will first run this exercise on MiniAOD format, then run it again on NanoAOD.

MiniAOD

The MINIAOD format was introduced at the beginning of Run 2 to reduce the information and file size from the AOD file format.
This means that several redundant versions of Ntuples for different analysis groups are stored in the limited CMS storage spaces.
For Run 2 analyses, most of the analysis groups at CMS skimmed the centrally produced MiniAOD files into smaller, analysis-specific ROOT Ntuples.

MiniAOD events contain two trigger products that we will need in these exercises.
The TriggerResults product contains trigger bits for each HLT path, whereas the TriggerObjectStandAlone product contains the trigger objects used at HLT.
In addition, the trigger prescales, L1 trigger decisions, and L1 objects are stored in MiniAOD.
A more detailed description of the trigger-related MiniAOD event content can be found here.

In this exercise we work with a skimmed MiniAOD file. (In case you are wondering where this skimmed file came from: it has been created using the configuration in ShortExerciseTrigger/test/skim_pfmet100.py, which selects events with offline MET above a threshold of 100 GeV.)

Inspect MiniAOD content

First, inspect the contents of the skimmed MiniAOD input file as follows:

edmDumpEventContent root://cmseos.fnal.gov//store/user/cmsdas/2023/short_exercises/Trigger/skim_pfmet100_SingleElectron_2016G_ReReco_87k.root --regex=Trigger

You can also inspect the full file content by dropping the –regex parameter.

As you see, there are indeed multiple TriggerResults products here, as well as other trigger-related collections.
We will learn how to interact with these two products and how to use their packed information in our physics analyses in this and the following exercises.

Extract the MET turn-on of the MET HLT path

Glimpse through the configuration file in test/ana_METMiniAOD.py and try to get a general idea of what it does.
Lines 45-48 of this configuration file show that it invokes an analyzer module called METTrigAnalyzerMiniAOD, and gives it two HLT paths as input parameters (with a specific version number that keeps track of minor updates to the HLT path from one menu to another one):

process.metTrigAnalyzerMiniAOD = cms.EDAnalyzer("METTrigAnalyzerMiniAOD")
process.metTrigAnalyzerMiniAOD.refTriggerName = cms.untracked.string("HLT_Ele27_eta2p1_WPTight_Gsf_v7")
process.metTrigAnalyzerMiniAOD.sigTriggerName = cms.untracked.string("HLT_PFMET170_HBHECleaned_v6")

We will use the METTrigAnalyzerMiniAOD analyzer module to perform a simple trigger efficiency measurement.
We will use HLT_Ele27_eta2p1_WPTight_Gsf as a reference trigger to measure the trigger efficiency of the HLT_PFMET170_HBHECleaned signal trigger.

Next, check through the code in plugins/METTrigAnalyzerMiniAOD.cc and try to get a general idea of what it does.

Then run the configuration file as follows:

cd test
voms-proxy-init --voms cms
cmsRun ana_METMiniAOD.py 

Use the TBrowser to explore the file histos_METTrigAnalyzer.root and look at the histograms h_met_all and h_met_passtrig.

Question

Can you explain the shape of each distribution?

The plotting macro plot_trigeff_met.C uses the TEfficiency class to create an efficiency plot using the two histograms produced above.

Inspect the code in plot_trigeff_met.C and then run this macro as follows:

root -l plot_trigeff_met.C

The resulting turn-on is the result of our efficiency measurement.
It shows the trigger efficiency of the signal trigger (vertical axis) in bins of offline-reconstructed MET (horizontal axis).

Question

Inspect the resulting efficiency plot. At which value of offline MET does the trigger turn-on reach its maximal value, and the flat “plateau” region start?

NanoAOD

A centrally maintained NanoAOD format was proposed in 2018, aiming for a common Ntuple format that can be used by most of the CMS analysis groups. Information about the NanoAOD format can be found here.

In this exercise, we will repeat the trigger efficiency measurement using a NanoAOD input file (events stored in it are different from the MiniAOD file used earlier) format and the related tools.

First, check out the NanoAOD-tools package for reading NanoAOD files:

cd $CMSSW_BASE/src
git clone https://github.com/cms-nanoAOD/nanoAOD-tools.git PhysicsTools/NanoAODTools
cd PhysicsTools/NanoAODTools
scram b -j 4
cd $CMSSW_BASE/src/LPCTriggerHATS/ShortExerciseTrigger/test

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 .

Then, first run the code (written using packages of NanoAOD tools available centrally) to produce the required histograms to compute the efficiency.
Next run the code to take the ratio of the two histograms and plot the efficiency and store it as a pdf:

python MET_Efficiency_NanoAOD_gp.py
python MET_Efficiency_Plotting.py

Question

Inspect the resulting efficiency plot. Though we used a different sample, is the shape of this result consistent with what you obtained in MiniAOD?

Key Points