Data Products and Processing Tutorial

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Tutorial Session Goal

This tutorial will explore the data products used in Mu2e and the modules and algorithms which create them. It is part of the June 2019 Computing and Software tutorial

Session Prerequisites and Advance Preparation

This tutorial assumes knowledge of art and the Mu2e detector. You will need to understand basic principles of how modules and event processing function in art. You will need to understand C++ data structures and fundamental types. You should have completed the following tutorials:

Session Introduction

The information content of Mu2e is stored in the form of art data products. There are several levels of information:

  • Monte Carlo generator information
  • Geant4 information
  • Digitized detector data, or digis (Offline format)
  • Reconstructed data

We will explore a few of these, and the algorithms which create them.

Exercises

General

Setup a satellite release for data exploration exercises: 

> setup mu2e
> cd $TUTORIAL_BASE/DataExploration
> $TUTORIAL_OFFLINE/v7_4_1/SLF6/prof/Offline/bin/createSatelliteRelease --directory .
> source setup.sh
> scons

This should produce a file lib/libmu2e_Examples_DataExplorer_module.so

Monte Carlo Generators

  • Mu2e generators and GenParticle class

Geant4 and Detector Simulation

  • The G4 Mu2e Detector description text files
  • Examine the SimParticle and StepPointMC classes
  • Virtual detectors

Digitized signals

The term 'digi' refers to the digitized detector data stored during Mu2e operations by the Data Acquisition (DAQ) system.

Exercise 1: Tracker digis

Histogram the number of digis in a pure μ- → e- conversion sample: 

> mu2e -c Examples/fcl/Ex01.fcl $TUTORIAL_DATA/dig.mu2e.CeEndpoint.MDC2018b.001002_00000001.art
> root -l Ex01_CeE.root
root [1] DE->Get("NStrawDigis")->Draw();

You should see ~40 StrawDigis/event on average. Now try with a μ- → e- conversion sample with beam backgrounds mixed in: 

> mu2e -c Examples/fcl/Ex01.fcl $TUTORIAL_DATA/dig.mu2e.CeEndpoint-mix.MDC2018d.001002_00000000.art

You should see around 2300 StrawDigis/event. The signal/noise for raw data is < 2% ! This is why we need background rejection and pattern recognition.

Bonus question: what is the format and what are the fields in the data collection file name and what do they mean? Hint: use the Mu2e wiki!

Now look at the TDC and ADC spectra: 

> root -l Ex01_CeE.root
root [] DE->Get("tdc")->Draw();
root [] DE->Get("deltatdc")->Draw();
root [] DE->Get("tot")->Draw();
root [] DE->Get("adc")->Draw();

The histograms will not have the correct range. By looking at Mu2e doc 4914, figure out what the ranges should be and correct the histograms marked with FIXME!. You can see how the values are accessed from the data product. Use your favorite editor (vim, emacs, ...) to edit the file. 

> vim Examples/src/DataExplorer_module.cc
...

Bonus questions: what is the physical meaning of deltatdc? tot? cal and hv?

Exercise 2: Calo digis

In this exercise, we'll explore calo crystal and calo cluster digis. We will start with a few questions:

1) Which data product contains crystal hits? How can you find the time / energy of a hit?
Answer: The data product is CaloCrystalHit, described in RecoDataProduct/inc/CaloCrystalHit.hh. The member functions time() and energy() give the corresponding information.

2) Which modules produce calorimeter clusters. What is the difference between them? Which data product should you use?
Answer: CaloProtoClusterFromCrystalHits and CaloClusterFromProtoCluster. CaloProtoClusterFromCrystalHits forms simply connected clusters from calorimeter hits. CaloClusterFromProtoCluster combines proto clusters close in time / distance into final clusters. You should use CaloClusters.

3) How do I know if a cluster is formed from many proto-clusters?
Answer: The boolean variable isSplit is true if the cluster is made from several proto-clusters

4) How do I access the list of crystal hits forming a cluster!
Answer: The caloCrystalHitsPtrVector is a vector containing a list of art::Ptr to the CaloCrystalHits.


Now that you are warmed up, we'll make a few plots (I know, this is getting so exciting!). First run the following snippet to produce the required data, then load the TTree in memory. 

> mu2e -c Examples/fcl/Ex02.fcl $TUTORIAL_DATA/dig.mu2e.CeEndpoint.MDC2018b.001002_00000001.art
> root -l ExploreCaloDigis.root
> TTree *calo = (TTree*) _file0->Get("CaloDigiDump/Calo")

Now histogram the energy of all crystal hits (switching to log scale is a good idea here): 

 > calo->Draw("cryEdep")

You should a rapidly falling distribution, as most hits are low energy. Now let's plot the crystal hits only in the second disk (first disk ID=0, second disk ID=1) 

> calo->Draw("cryEdep","crySectionId==1")

Can you histogram the position of each crystal hit? 

> calo->Draw("cryPosY:cryPosX","","box")

You should see 674 boxes... the bigger the box, the larger the number of hits in the crystal. As expected, there are more hits in the central region.

You should be on fire at this point, so we'll look at the cluster. Let's plot the number of crystals in the cluster

Next, draw the energy of all clusters with a radius of the center-of-gravity greater than 400 (less than 400) 

> calo->Draw("cluEnergy","sqrt(cluCogX**2+cluCogY**2)>400")
> calo->Draw("cluEnergy","sqrt(cluCogX**2+cluCogY**2)<400")

There is a lot of noise below 400! What about clusters in disk 0 and disk 1? Can we plot both on the same plot? 

> calo->Draw("cluEnergy","cluSplit==0")
> calo->Draw("cluEnergy","cluSplit==1","same")

As expected, disk 1 is cleaner!

Bonus: If you feel audacious, try to code a module doing the following: 

> Plot the energy and time of all crystal hits in the microbunch
> Plot the energy of all clusters with a radial location greater than 400 mm. 
> Plot the energy of clusters containing a single proto-cluster or several proto-clusters in two separate histograms   
> Plot the energy of the most energetic hit in the cluster

An implementation is shown in Examples/src/CaloDigiDump_module.cc

Hit Reconstruction

  • Track reconstruction algorithms and data products
    • Hit Reconstruction
    • Time Clusters
    • Helices
    • Kalman Fit
  • Calorimeter reconstruction algorithms and data products
  • CRV reconstruction algorithms and data products

Reference Materials

Glossary of Raw and Reconstructed Data Products

class description contents
StrawDigi Offline format of a single Tracker hit TDC and TOT from both straw ends, ADC waveform
ComboHit Calibrated Tracker hit, or an aggregate of several hits position in space, time, and time differences
TimeCluster Collection of ComboHits nearby in time and (roughly) space average time and error
HelixSeed Helix interpretation of a subset of hits in a TimeCluster Helix parameters, t0, ComboHits with position along the helix
KalRep Full Kalman filter fit result: not persistable Complete set of weight and parameter matrices and vectors used in the fit
KalSeed Compact summary of the Kalman filter fit result Sampled fit segments, associated straw hits and straws
KalSegment KalSeed component: local fit result Fit parameters and covariance at a particular point
TrkStrawHitSeed KalSeed component: straw hit as used in fit hit position, residual, time, drift radius, errors, ...
TrkStraw KalSeed component: straw intersected by the fit strawID, DOCA to wire, radiation length, energy loss, ...
CaloCluster Cluster of calorimeter crystal energy deposits Total energy, center of gravity (COG), energy moments
CrvCoincidenceCluster Cluster of adjacent CRV reco pulses position, PE count, start and end times

Glossary of Principle Reconstruction Modules

module category description
StrawDigisFromStepPointMCs Simulation Converts G4 straw energy deposits into StrawDigs
StrawHitReco Reconstruction Converts StrawDigs into single-straw ComboHits
CombineStrawHits Reconstruction Combines adjacent ComboHits in a panel into aggregate ComboHits
FlagBkgHits Reconstruction Identify (flag) panel ComboHits likely produced by low-energy Compton or delta-ray electrons
TimeClusterFinder Reconstruction Group time-adjacent panel ComboHits (and calorimeter cluster if available) into a cluster
RobustHelixFinder Reconstruction Fit a cluster of panel ComboHits to a simple helix using space-point positions
CalTimePeakFinder Reconstruction Group panel ComboHits near a calorimeter cluster in time into a cluster
CalHelixFinder Reconstruction Fit the calorimeter cluster position, target position and panel ComboHits to a simple helix
KalSeedFit Reconstruction Fit single-straw transverse wire positions to a helix, using a simple helix as starting point
KalFinalFit Reconstruction Kalman filter fit of single-straw drift ellipses, constrained with calorimeter cluster time (if present)
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