Data Products and Processing Tutorial

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:

• Mu2e detector overview
• Mu2e_Offline_Tutorial
• Running_Art_Tutorial

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
> /cvmfs/mu2e.opensciencegrid.org/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

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

• Mu2e doc 4914

Glossary of 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)