TrkAna

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Overview

TrkAna is a track-based ROOT TTree that can be used to help with analysis. Each entry in the TTree corresponds to a single fitted track and contains reconstructed information from the tracker, calorimeter and CRV. There is also the option to write out Monte Carlo truth information and additional information for other track types that might be important to an analysis.

How to Get Help

Feel free to send any questions to the #trkana Slack channel, or the 'Is It Me Or A Bug?' hypernews forum.

There is also a tutorial that is a few years old and will be updated soon.

For Analyzers

How To Create a TrkAna Tree

You can use the TrkAna Musing like so:

setup mu2e
muse setup TrkAna
mu2e -c TrkAna/fcl/TrkAnaReco.fcl -s mcs.art --TFileName trkana.root

where mcs.art contains the result of the Mu2e reconstruction.

In the trkana.root file, you will see two different TrkAna-related folders:

  • TrkAnaNeg contains the results of the negatively-charged tracks, and
  • TrkAnaPos contains the results of the positively-charged tracks.

TrkAna is designed to be flexibile so if this does not produce the type of tree you want, then there are fcl parameters that can be changed. A few examples are available here: https://github.com/Mu2e/TrkAna/tree/main/fcl and are given in the table below. Also, you can always ask for help on the #trkana channel on Slack

fcl file module label candidate supplements notes
TrkAnaReco.fcl TrkAnaNeg downstream e-minus upstream e-minus, downstream mu-minus
TrkAnaPos downstream e-plus upstream e-plus, downstream mu-plus
CrvExpert.fcl TrkAnaNeg downstream e-minus upstream e-minus, downstream mu-minus adds crvinfo, crvinfomc, crvsummary, crvsummarymc, and crvinfomcplane branches
TrkAnaPos downstream e-plus upstream e-plus, downstream mu-plus
TrkAnaReco_Upstream.fcl TrkAnaNeg upstream e-minus downstream e-minus, upstream mu-minus
TrkAnaPos upstream e-plus downstream e-plus, upstream mu-plus
TrkAnaReco_ceSimReco.fcl TrkAnaNeg downstream e-minus none for use with reco files generated with ceSimReco.fcl
fcl files below are flagged for updating / removal
TrkAnaRecoEnsemble-MC.fcl TrkAnaNeg downstream e-minus upstream e-minus, downstream mu-minus for use with MDC2018 Ensemble datasets
TrkAnaPos downstream e-plus upstream e-plus, downstream mu-plus
TrkAnaRecoEnsemble-Data.fcl TrkAnaNeg downstream e-minus upstream e-minus, downstream mu-minus for use with MDC2018 Ensemble datasets, no MC information
TrkAnaPos downstream e-plus upstream e-plus, downstream mu-plus
TrkAnaReco_wTrkQualFilter.fcl TrkAnaNeg downstream e-minus upstream e-minus, downstream mu-minus an art filter module is run before creating the TrkAna tree
TrkAnaPos downstream e-plus upstream e-plus, downstream mu-plus
TrkAnaReco_MultipleTrkQual.fcl TrkAnaNeg downstream e-minus upstream e-minus, downstream mu-minus an additional TrkQual algorithm is used for each track
TrkAnaPos downstream e-plus upstream e-plus, downstream mu-plus

How to Analyze a TrkAna Tree

Each of these contain TTrees that you can use ROOT or python

with ROOT

You can use the trkana tree like you would any other ROOT TTree: with the ROOT command line for data exploration, with ROOT macros to make plots etc.

with Python

  • Note: the below is for the latest version of uproot4

The best way to open ROOT files in Python is to use the uproot package. It does not require a working installation of ROOT, so it allows to read a ROOT file on basically any platform that supports Python (e.g. Colaboratory). It also provides a more pythonic bridge between ROOT and Numpy/pandas.

In order to open a ROOT TTree with uproot it's sufficient to write:

import uproot
trkananeg = uproot.open("trkana.root:TrkAnaNeg/trkana") # opens the 'trkana' tree in the 'TrkAnaNeg' folder

Now, if we want to store, for example, the deent branch in the dataframe:

df = tree.arrays(filter_name=['deent*'],library="pd")


Once your data is stored in a pandas dataframe or in a numpy array you can plot it with the many plotting libraries available (matplotlib, plot.ly, etc.). This example shows how to plot an histogram with matplotlib:

import uproot
import matplotlib.pyplot as plt

trkananeg = uproot.open("trkana.root:TrkAnaNeg/trkana")
df = trkananeg(filter_name=['deent*'],library="pd")
plt.hist(df["deent.mom"], bins=60, range=(95,110))

A class that allows you to query the dataframe and plot manipulated variables can be found at https://github.com/soleti/mu2e_plotter.

For Developers

If you will be contributing to the development of TrkAna, then you can fork TrkAna on GitHub and build it within Muse Like so:

Once you log in:

setup mu2e
cd /your/working/directory
git clone git@github.com:<Your GitHub Username>/Offline.git (or muse backing Offline vXX_YY_ZZ)
git clone git@github.com:<Your GitHub Username>/TrkAna.git
muse setup
muse build -j4 --mu2eCompactPrint

Then you can make your developments and put in a pull request.

Version History / TrkAna Musings

This is the list of TrkAna Musings. The top row corresponds to the "current" version.

TrkAna Musing TrkAna tag muse backings muse stub notes
v04_00_00 v04_00_00 SimJob MDC2020z --> Offline v10_23_01 sl7-prof-e28-p049 breaking changes: demfit[] branch, new genealogy branches
(not built) v03_00_01 N/A N/A tag before major breaking change, just code tidying so no associated musing
v03_00_00 v03_00_00 SimJob MDC2020y --> Offline v10_22_01 sl7-prof-e20-p040 breaking changes: branch names now three characters; uses KinKal fits
v02_03_00 v02_03_00 SimJob MDC2020w --> Offline v10_21_00 sl7-prof-e20-p039
v02_02_00 v02_02_00 SimJob MDC2020v --> Offline v10_20_00 sl7-prof-e20-p035
v02_01_00 v02_01_00 SimJob MDC2020u --> Production v00_10_00 --> Offline v10_18_00 sl7-prof-e20-p033
v02_00_01 v02_00_01 SimJob MDC2020t --> Production v00_09_11 --> Offline v10_17_00 sl7-prof-e20-p026
(not built) v02_00_00 N/A N/A failed build due to incompatible KinKal version
v01_01_00 v01_01_00 SimJob MDC2020r --> Production v00_09_02 --> Offline v10_15_01 sl7-prof-e20-p023 adds TrkAnaUtils
(not built) v01_00_01 N/A N/A resolves MDC2020r incompatibility
v01_00_00a v01_00_00 Production v00_07_00 --> Offline v10_11_00 sl7-prof-e20-p021 updated for muse 1-path/2-path change
v01_00_00 v01_00_00 Production v00_06_00 --> Offline v10_10_01 sl7-prof-e20-p018 uses deprecated "muse link"

Tree Structure

The TrkAna tree structure is documented in google sheet (spreadsheet). There are separate sheets for different major version numbers. Links to the existing sheets are below. These are viewable by anyone; if you would like to comment or edit these sheets please send a message to the #TrkAna slack channel. Note that the sheets have multiple tabs, at the bottom of the page. The main tab lists the branches, while the other tabs list the detailed content (leafs) of the individual branches, by name.

Hit Level Information

By setting the diagLevel to 2 when running TrkAna, you will fill hit level information branches. If you are processing Monte Carlo information, hit level Monte Carlo truth branches will also be added. Adding these increases the size of the output file by an order of magnitude.

Future Developments

We're always looking for input from developers and analyzers to keep TrkAna up-to-date! Get in touch on the #trkana Slack channel, or with Andy Edmonds directly with any thoughts / comments you have