TrkAnaTutorial

Under Construction!

This tutorial is currently being written

Tutorial Session Goal

A TrkAna tree is a ROOT TTree where each entry in the tree represents a single track. The TrkAna tree is created by the TrackAnalysisReco module of Mu2e Offline which runs over a KalSeedCollection.

In this tutorial you will:

• create TrkAna trees using the Mu2e Offline software; and,
• analyze them using the ROOT command line and ROOT macros.

Session Prerequisites

This tutorial should be useful for anyone starting out with TrkAna tree analysis

Before starting this tutorial you should:

• know about the physics of Mu2e;
• have the appropriate docker container set up; and,
• know how to run the Mu2e Offline software and ROOT

Basic Exercises

Exercise 1: Creating a simple TrkAna tree

In this exercise, we will create a simple TrkAna tree and investigate it with the ROOT command line.

1. First, run mu2e on a single CeEndpoint-mix reco art file:
> mu2e -c $TUTORIAL_BASE/TrkAna/fcl/TrkAnaTutEx01.fcl -S $TUTORIAL_BASE/TrkAna/filelists/mcs.mu2e.CeEndpoint-mix-cat.MDC2018h.1-file.lst
2. Now let's have a look at the TrkAna tree with the ROOT command line
> root -l trkana-ex01.root root[n]: TrkAnaEx01->cd() root[n]: trkana->Print() You will see the TrkAna tree structure. Here is a brief description of the branches:
1. evtinfo: event level information (e.g. event ID of the event this track is from)
2. hcnt: hit count of different types of hit (e.g. number that pass certain collections)
3. tcnt: track count of different track types
4. trk: global fit information for the track (e.g. fit status, ranges of validity, number of hits, track quality)
5. trk(ent/mid/xit): local fit information for the track at the enttrance of the tracker, the middle of the tracker and exit of the tracker (e.g. fit momentum, pitch angle)
6. trktch: calorimeter hit information for the calorimeter function associated to the track (tch = TrkCaloHit)
7. crvinfo: information of associated hits in the CRV

Note that the "trk" parts of the branch names are configurable -- you will see this is a minute

3. Now we can plot some simple things:
   1. the track momentum at the tracker entrance
   root[n]: trkana->Draw("trkent.mom")
   2. the calorimeter cluster energy
   root[n]: trkana->Draw("trktch.edep")
   3. With this last command you will see some entries at -1000. This means that there is no associated calorimeter cluster for this track. To exclude these we want to want to add a cut on the trktch.active flag (0 = there is no TrkCaloHit, 1 = there is TrkCaloHit):
   root[n]: trkana->Draw("trktch.edep", "trktch.active==1")
4. Let's take a quick look at the fcl file to see how the TrackAnalysisReco module has been configured. Open it up in your favourite text editor and look at these important lines:
TrkAnaEx01 : { @table::TrackAnalysisReco } physics.analyzers.TrkAnaEx01.candidate.input : "KFFDeM" physics.analyzers.TrkAnaEx01.candidate.branch : "trk" physics.analyzers.TrkAnaEx01.diagLevel : 0 physics.analyzers.TrkAnaEx01.FillMCInfo : false In order, these lines:
1. import an example TrkAna module configuration (you can find it in $MU2E_BASE_RELEASE/TrkDiag/fcl/prolog.fcl);
2. define the input KalSeedCollection that we want a TrkAna tree for (KFFDeM = KalFinalFit Downstream eMinus);
3. configure the name of the output branches;
4. set TrkAna to use the lowest diagnostic level (0 = simple list of tracks, 1 = hit level diagnostics); and,
5. make sure we are not touching the MC truth
5. (Optional): Run on a CeplusEndpoint-mix file ($TUTORIAL_BASE/TrkAna/filelist/mcs.mu2e.CeplusEndpoint-mix-cat.MDC2018h.1-file.lst) and get a list of positively-charged tracks. What is the momentum of these tracks?
6. (Optional): Create a second instance of the TrackAnalysisReco module. Have one instance set to look at negatively-charged tracks and the other set to look at positively charged tracks. Run on muplusgamma-mix ($TUTORIAL_BASE/TrkAna/filelist/mcs.mu2e.flatmugamma-mix-cat.MDC2018h.1-file.lst) and count how many tracks of each type are found

Exercise 2: Calculating the Ce efficiency

Now that we can create a TrkAna tree, let's calculate how efficient we are at reconstructed conversion electrons with some signal cuts. Before starting this exercise, a quick note about event counting, event weighting, and track quality.

Event Counting

In the simulation, we generate a certain number of events. However, in order to save space, we only write out events that will produce a reconstructed tracks. We have various ways of filtering events but the result is the same -- the number of events in the output art files do not correspond to the number of events that were generated, which is what we need to calculate absolute efficiencies. To account for this, we keep track of the number of events that were generated by creating a GenEventCount object. Then we can run the genCountLogger module to read the actual number of generated events.

Event Weighting

In each "mixed" event, we add a single "primary" particle onto a set of "background frames", which represent the background hits from other processes. We want to simulate the variable intensity of the proton beam at the production target and so we scale the number of background hits when we create the mixed event. However, we still only add a single primary particle and so we record the scale factor used in a ProtonBunchIntensity object for use later. In this exercise, we will add a new module to the trigger path (PBIWeight), which translates the scale factor used for the proton bunch intensity into an EventWeight object. The TrackAnalysisReco then writes out these event weight values to a new branch (evtwt.PBIWeight). Event weighting is explored in more detail in Advanced Exercise #2.

Track Quality

We want a simple way to determine how well-reconstructed the tracks are. We use an artificial neural network (ANN) called TrkQual that takes various properties of the track and is trained to give each track a trkqual value between 0 (poorly-reconstructed) and 1 (well-recosntructed). In this exercise, we add the TrkQual modules to the trigger path and TrkAna writes out the output value to trk.trkqual. TrkQual is explored in more detail in Advanced Exercise #3.

The Exercise

Now onto the exercise:

1. Create a TrkAna tree with CeEndpoint-mix tracks and include the generated event count, proton bunch intensity event weights and track quality modules
mu2e -c $TUTORIAL_BASE/TrkAna/fcl/TrkAnaTutEx02.fcl -S $TUTORIAL_BASE/TrkAna/filelists/mcs.mu2e.CeEndpoint-mix-cat.MDC2018h.1-file.lst This fcl file has added the following lines: physics.TrkAnaTrigPath : [ @sequence::TrkAnaReco.TrigSequence ] physics.TrkAnaEndPath : [ genCountLogger, TrkAnaEx02 ] You can search for that parameter in $MU2E_BASE_RELEASE/TrkDiag/fcl/prolog.fcl but simply it adds the PBIEventWeight module (PBI = ProtonBunchIntensity) and TrkQual outputs
2. here is an example ROOT macro that plots the track momentum onto a histogram with 0.5 MeV wide bins
root -l TrkDiag/test/TrkAnaTutEx02.C
3. Add the following signal cuts to the Draw function
1. the fit is successful (trk.status > 0)
2. the track is in the time window of 700 ns -- 1695 ns (trk.t0)
3. the tan-dip of the track is consistent with coming from the target 0.577350 -- 1.000 (trkent.td)
4. the impact parameters of the track is consistent with coming from the target -80 mm -- 105 mm (trkent.d0)
5. the maximum radius of the track is OK 450 mm -- 680 mm (trkent.d0 + 2./trkent.om)
6. the track is of good quality (trk.trkqual > 0.8)
4. Because we simulated each event with a different proton bunch intensity, each track should be weighted by the PBIWeight. To do this you will want to modify the cut command to add the event weighting:
evtwt.PBIWeight*(cuts)
5. Now we can count the number of tracks that pass all these cuts
hRecoMom->Integral()
6. We can also integrate in the momentum signal region. Be careful TH1F::Integral takes bin numbers as its arguments and not x-values. You can find a bin for a given x-value with hist->GetXaxis()->FindBin(x-value). Be sure to make sure you don't go one bin too high!
7. To calculate the efficiency you need to know the number of events generated for this simulation: genCountLogger
8. calculate the Ce efficiency
9. An example solution macro can be found in TrkDiag/test/TrkAnaTutEx02Soln.C
10. (Optional): plot results and TLines on momentum plot, can you change cut and lines follow
11. (Optional): increase the track quality cut and plot the momentum distribution of both on the same set of axes with different colours

Exercise 3: Adding MC truth

Because we are running on simulated data, we know the truth of what happened. How well is our detector doing?

1. run TrkAnaReco.fcl to add MC truth
2. plot reco - truth for momentum or something
3. do a double-sided crystal ball fit

Exercise 4: Following genealogy

Can also see the important steps in the genealogy (run on a different MDC2018 sample?). Explain difference between primary and gen branches

1. something

For any other intermediate steps in the genealogy, you will need to run Offline.

Exercise 5: Adding supplemental tracks

There might be other tracks that are important to your analysis (e.g. upstream going tracks)

1. run with supplemental tracks
2. look for reflected tracks? compare DeM to DmuM?
3. check for CRV coincidence?

Conclusion

This last exercise created a TrkAna tree that is the same the one created in TrkAnaReco.fcl

Advanced Exercises

Exercise 3: Event weighting?

Run on flateminus-mix with DIO weights and plot that

Reference Materials

• Use this place to add links to reference materials.
• TrkAna wiki page

A Useful Glossary

ROOT

data analysis framework developed at CERN

KalSeed

data product that represents a track

CeEndpoint-mix

dataset name for CeEndpoint (i.e. mono-energetic electrons) with background frames mixed in

CeplusEndpoint-mix

dataset name for CeplusEndpoint (i.e. mono-energetic positrons) with background frames mixed in

flatmugamma-mix

dataset name for flatmugamma (i.e. flate energy photons generated at muon stopping positions) with background frames mixed in

KalFinalFit

the module name for the final stage of the Kalman filter fit for the track

TrkQual

an artificial neural network (ANN) that takes parameters from the track and outputs a value between 0 (poorly reconstructed) and 1 (well-reconstructed)