TrkAnaTutorial

Under Construction!

This tutorial is currently being written

Session Prerequisites

This tutorial is aimed at anyone starting ntuple 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.

Session Introduction

One of the final outputs of the Mu2e reconstruction are fits to the tracks in the tracker. These are stored as KalSeeds in KalSeedCollections. For each fit hypothesis, we have a different KalSeedCollection (e.g. downstream electrons, downstream muons, upstream positrons).

In order to do analyses with these tracks, we have an art module that creates a ROOT TTree of these KalSeeds called TrkAna. Each entry in the tree corresponds to a single track.

In this tutorial you will:

create TrkAna trees using the Mu2e Offline software and MDC2018 datasets; and,
analyze them using the ROOT command line and ROOT macros.

Basic Exercises

These exercises are designed to be run on v7_4_1 from $TUTORIAL_BASE/TrkAna

> whatever command to setup in docker
> cd $TUTORIAL_BASE/TrkAna

Exercise 1: Creating the simplest TrkAna tree

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

First, run mu2e on a single CeEndpoint-mix reco art file:
Now let's have a look at the TrkAna tree with the ROOT command line

evtinfo: event level information (e.g. event ID of the art::Event this track is from)
hcnt: hit count of different types of hit (e.g. number that pass certain selections)
tcnt: track count of different track types
trk: global fit information for the track (e.g. fit status, ranges of validity, number of hits)
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)
trktch: calorimeter hit information for the calorimeter function associated to the track (tch = TrkCaloHit)
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

Now we can plot some simple things:
1. the track momentum at the tracker entrance
2. the calorimeter cluster energy
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):
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:

import an example TrkAna module configuration (you can find it in $MU2E_BASE_RELEASE/TrkDiag/fcl/prolog.fcl);
define the input KalSeedCollection that we want a TrkAna tree for (KFFDeM = KalFinalFit Downstream eMinus);
configure the name of the output branches;
set TrkAna to use the lowest diagnostic level (1 = simple list of tracks, 2 = hit level diagnostics); and,
make sure we are not touching the MC truth

That's the end of this exercise -- you can now create a simple TrkAna tree! Try some of the following optional exercises to explore further:

(Optional): Run on a CeplusEndpoint-mix file (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?
(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 (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 our reconstruction is for conversion electrons with some signal cuts. There's a lot of concepts introduced in this exercise, so don't worry if it all doesn't make sense at first. I'll start with some quick notes on event counting, event weighting, and reco 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. We need to know the total number of generated events in order to calculate an absolute efficiency. We keep track of the number of events that were generated by creating a GenEventCount object for each SubRun. Then in our TrkAna job, we 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 our TrkAna job, 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 module will write out these proton beam scale factors to a new branch (evtwt.PBIWeight). Event weighting is explored in more detail in Advanced Exercise 4.

Reco Quality

We use various algorithms to check the "quality" of a track in some dimension. Currently there are two we consider:

the track quality (i.e. how will-reconstructed a track is); and
the particle ID (PID) quality (i.e. how closely a track resembles an electron rather than a muon).

In our TrkAna job, we will add two artificial neural network (ANN) based algorithms to determine these reco qualities (TrkQual and TrkCaloHitPID). In this exercise, we only care that the output of both of these modules is a RecoQualCollection with one RecoQual object per KalSeed. A RecoQual is essentially a float and, in these two algorithms, is between 0 (poorly-reconstructed, XXX PID) and 1 (well-reconstructed, YYY PID). TrkQual is explored in more detail in Advanced Exercise 3.

The Exercise

Now onto the exercise:

Create a TrkAna tree with CeEndpoint-mix tracks and include the genCountLogger, PBIWeight, TrkQual and TrkCaloHitPID modules

PBIWeight

Also in TrkAnaTutEx02.fcl, you will see that we have changed the input parameter to just "KFF"and added the suffix parameter:

DeM

DeP

If you open up the ROOT file and print the tree structure, you will notice the following new branches:

evtwt: stores the values of all EventWeight objects in the art event
trkqual: stores the values of all relevant RecoQual objects in the art event

Defining a suffix in the TrkAna configuration allows us to store only the relevant RecoQual values.

(Optional): Try running without a suffix parameter and set input back to "KFFDeM". What do you see in the output trkqual branch
Run this example ROOT macro that plots the track momentum onto a histogram with 0.5 MeV wide bins
Change the bin width to 0.05 MeV
Add the following signal cuts to the Draw function

the fit is successful (trk.status > 0)
the track is in the time window of 700 ns -- 1695 ns (trk.t0)
the tan-dip of the track is consistent with coming from the target: 0.577350 -- 1.000 (trkent.td)
the impact parameter of the track is consistent with coming from the target: -80 mm -- 105 mm (trkent.d0)
the maximum radius of the track is OK: 450 mm -- 680 mm (trkent.d0 + 2./trkent.om)
the track is of good quality (trkqual.TrkQualDeM > 0.8)

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:
Now we can count the number of tracks that pass all these cuts
We can also integrate in the momentum signal region with the same function. Be careful TH1F::Integral takes bin numbers as its arguments and not x-values. Some hints:

you can find a bin for a given x-value with hist->GetXaxis()->FindBin(x-value)
make sure you aren't off by one bin by checking the bin low edges and bin high edges with TAxis::GetBinLowEdge() and TAxis::GetBinUpEdge().

To calculate the efficiency you need to know the number of events generated for this simulation. This is stored in the output of the genCountLogger module
Now you can calculate the absolute Ce efficiency. What's the answer?

Now that you can calculate the Ce efficiency, try some of the following exercises:

(Optional): Make the plot prettier by:

adding appropriate axis labels
writing the Ce efficiency on the plot with a TLatex
adding dashed lines to show the momentum window with TLines (extra bonus: have the lines move when the momentum window values change)

(Optional): Add a second momentum plot but with a higher track quality cut and include a TLegend.

Exercise 3: Adding MC truth

In this exercise, we will add MC truth information to the TrkAna tree and see how close our reconstruction matches the truth.

Create a TrkAna tree with CeEndpoint-mix tracks and include the MC truth information:

fillMCInfo

candidate.fillMC

You can open up the file and Print the tree structure. For this exercise, we will focus on the trkmcent, which contains the MC information for the step that crossed the entrance into the tracker. The other trkmc* branches will be the focus of Exercise 4.
Run this example ROOT macro that plots the intrinsic tracker momentum for tracks that pass our signal cuts except for the trkqual and momentum window cuts
Add the following line to the start of the macro:
Using what you learned in Exercise 2, count the number of events above +3 MeV, including the events in the overflow bin
Add the trkqual cut and play with it. How does the number of events above +3 MeV change?

(Optional): Do the same comparison at the middle or exit of the tracker
(Optional): Perform a fit to a double-sided crystal ball function. This function is a Gaussian with polynomial tails. The function can be found here: scripts/dscb.h. You will need to create your own TF1 (hint) and use the TH1::Fit() function (hint). What is the core resolution?

Exercise 4: Following genealogy

With TrkAna, we also have access to important steps in the genealogy of the track (i.e. which particles produced the track). Before looking at the branches themselves, we need to discuss a little about the simulation.

For each event in the simulation we instantiate a GenParticle, which represents the particle we want to start the simulation with. For some of our samples, we create more than one of these per event (e.g. for cosmic rays, we take all the particles created in the shower at a certain altitude). We then pass these GenParticles to the physics simulation and create a SimParticle for each one. We then let Geant4 simulate these SimParticles and create more SimParticles until we end up with StepPointMCs in the detectors. For the tracker, there will be many different StepPointMCs from different SimParticles in every straw. We assign a SimParticle to be responsible for each straw hit if it had the most StepPointMCs in that hit.

Now here is a description of the branches we have in TrkAna:

trkmc: contains information about the SimParticle that produced the most hits on the track
trkmcgen: contains information about the actual GenParticle in our simulation that ultimately produced the track (e.g. for cosmic rays, this will be the particle in the shower that ultimately produced the track)
trkmcpri: contains information about the primary particle that produced the trkmcgen particle (e.g. for cosmic rays, this will correspond to the cosmic ray that produced the shower (i.e. the proton))

For most of our samples, these are all the same particle. In order to explore the differences, we will run on a sample of cosmic rays made by the CRY generator.

Create a TrkAna tree with CRY-cosmic-general-mix tracks and include the MC truth information:
Open the ROOT file and look at the PDG ID codes of the MC particles (defined here but some important ones are 11 = electron, -11 = positron, 13 = muon, -13 = positive muon, 2212 = proton).

GenParticles

However just because we have two particles of the same type at the different stages, does not mean that they are the same particle. We need look at the trkmc.prel, which encodes the relationship between the track particle and the GenParticle:
Anything else to do here?

That's it for this exercise. As you can tell, this isn't an exhaustive genealogy tree so if you need to look at something more complex, then you can run in the full Offline framework where we (currently) store every step in the genealogy for saved tracks. However, you can try this optional exercise for another example:

(Optional): Run on the flatmugamma-mix (internalRMC would be better...) file list and look at the genealogy of those tracks

Exercise 5: Adding supplemental tracks

From the last exercise, we can see that we get muon tracks. But we were only looking at the result of the electron-hypothesis fit. We can have TrkAna write out the results of "supplement" fits (e.g. downstream muon track)

Create a TrkAna tree with CRY-cosmic-general-mix tracks and include the result of downstream mu-minus fits:
Open up the fcl file and you will see that we have created a couple of blocks to handle branch definitions:

de

KalSeed

Open up the ROOT file and look in the tree. You will see that we now have both de* branches and dm* branches
Plot resolution for DeM with and without demc==11 and demc==13
add truth for DmuM and do the same thing
Compare dequal.TrkQualDeM and dmqual.TrkQualDmuM (or TrkCaloHitPIDs?)
(Optional): swap candidate and supplement
(Optional): add ue

Exercise 6: TrkAnaReco

Run TrkAnaReco.fcl and explain branches. The detrkqual and detrkpid branches will be exaplained in an advanced exercise.

Run TrkAnaReco.fcl
look for reflected tracks? compare DeM to DmuM?
check for CRV coincidence?

Advanced Exercises

These exercises are designed to be run on v7_4_1 from $TUTORIAL_BASE/TrkAna

> whatever command to setup in docker
> cd $TUTORIAL_BASE/TrkAna

Exercise 1: Looking at the hits in the track

With TrkAna we can get information about the individual hits by setting diagLevel to 2

This #includes TrkAnaReco.fcl and then edits the diagLevel

Exercise 2: Analyzing a TrkAna tree with a compiled macro

Write a compiled macro that loops through each event and uses the TrkInfo branches. This can just run TrkAnaReco.fcl

Exercise 3: Retraining TrkQual

This can just run TrkAnaReco.fcl but want to highlight that the TrkQualInfo is written out with the candidate.trkqual Write out trkqual branch and retrain with a different background weight expression (or a different sample?) Run in parallel with original TrkQual

Exercise 4: Estimating the DIO background (with event weighting)

Run on flateminus-mix with DIO weights and plot that

Exercise 5: Creating your own "TrkAna" tree using InfoStructs

For this exercise, you should know how to write an art module.

Explain info structs and info struct helper to create a new module to create a simple tree (need to know module writing)?

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)

TrkAnaTutorial

Contents

Under Construction!

Session Prerequisites

Session Introduction

Basic Exercises

Exercise 1: Creating the simplest TrkAna tree

Exercise 2: Calculating the Ce efficiency

Exercise 3: Adding MC truth

Exercise 4: Following genealogy

Exercise 5: Adding supplemental tracks

Exercise 6: TrkAnaReco

Advanced Exercises

Exercise 1: Looking at the hits in the track

Exercise 2: Analyzing a TrkAna tree with a compiled macro

Exercise 3: Retraining TrkQual

Exercise 4: Estimating the DIO background (with event weighting)

Exercise 5: Creating your own "TrkAna" tree using InfoStructs

Reference Materials

A Useful Glossary

Navigation menu

TrkAnaTutorial

Under Construction!

Session Prerequisites

Session Introduction

Basic Exercises

Exercise 1: Creating the simplest TrkAna tree

Exercise 2: Calculating the Ce efficiency

Exercise 3: Adding MC truth

Exercise 4: Following genealogy

Exercise 5: Adding supplemental tracks

Exercise 6: TrkAnaReco

Advanced Exercises

Exercise 1: Looking at the hits in the track

Exercise 2: Analyzing a TrkAna tree with a compiled macro

Exercise 3: Retraining TrkQual

Exercise 4: Estimating the DIO background (with event weighting)

Exercise 5: Creating your own "TrkAna" tree using InfoStructs

Reference Materials

A Useful Glossary

Navigation menu

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