ALICEPCGTutorial
  • Introduction
  • Introduction
    • Welcome
    • Git
    • General Naming Scheme and Analysis Tasks
    • General Afterburner Introduction
    • Analysis Notes and Papers
  • AliPhysics Implementation and GRID Running
    • GRID and AliRoot/AliPhysics
    • Running AnalysisTasks
    • Supporting Classes and Cut Numbers
    • Integration of Dataset/MC
    • LEGO Trains
    • Download Files from GRID
    • The EMCal Correction Framework
  • Quality Assurance and Energy Calibration of Calorimeters
    • Overview
    • EventQA
    • PhotonQA
    • ClusterQA
    • PrimaryTrackQA
    • Energy Calibration of Calorimeters
    • TPC Spline Creation
  • Cocktail Running and External Input
    • Cocktail Framework Overview
    • Cocktail Framework Intro
    • Link collection from other PWGs
  • Neutral Meson and Direct Photon Analysis - Afterburners
    • Neutral Pion and Eta Analysis
    • Heavy Meson Analysis
    • Merged Cluster Analysis
    • Merged Analysis Toy Model for Momentum Resolution
    • Systematic Uncertainties
    • Combination of Measurements
    • Useful functions
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  1. Quality Assurance and Energy Calibration of Calorimeters

PhotonQA

PreviousEventQANextClusterQA

Last updated 3 years ago

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This part of the QA must be run for any PCM related analysis (for PCM and hybrid analyses PCM-EMCal, PCM-PHOS, PCM-DCal)

The photon QA includes all cut variables in the context of conversion photon analysis:

generated histograms (list of examples) (full set of generated histograms can be deduced from the macros themselves/or from the output generated): 1. Electron level: pTp_TpT​, η\etaη, dE/dx, TPC clusters, findable TPC clusters,... 2. Photon level: pTp_TpT​, η\etaη, ϕ\phiϕ, α\alphaα, invariant mass, chi2, psi-pair,...

Running the PhotonQA(_Runwise).C will save the output into the following folder structure:

CUTNUMBER/SYSTEM/PhotonQA/

In addition, *.root files will be generated in CUTNUMBER/SYSTEM/ containing all the histograms as well.

important note Run QA_RunwiseV2.C first(!) with doEventQA = kTRUE and doPhotonQA = kTRUE - it will read TTrees from runwise output and generate runwise QA plots. It will also merge the output from different runs as this is, in general, impossible to do on TTree level due to the huge file sizes. Then run QAV2.C.

Carefully check all output from runwise histograms with special focus on data/MC comparison (Is the MC able to reproduce all QA histograms extracted from data? Does the MC follow the trends seen in data? Are there any suspicious runs or any observations that cannot be explained?...)

Some example plots for 1. and 2.:

  1. 2.