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Object Collection Cross Cleaning using PAT. SudhirMalik Fermilab/University of Nebraska-Lincoln. PAT Tutorial – CERN – 10 March 2010. Object Reconstruction with CMS. Physics Objects and POGs. Physics Objects are reconstructed independently by different POGs Muon Jet Electron Photon

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Object Collection Cross Cleaning using PAT


Fermilab/University of Nebraska-Lincoln

PAT Tutorial – CERN – 10 March 2010

Physics Objects and POGs

  • Physics Objects are reconstructed independently by different POGs

    • Muon

    • Jet

    • Electron

    • Photon

    • Tau

  • This can/will lead to ambiguities across different collections

    • Muon

    • Jet

    • Electron

    • Photon

    • Taus


  • Jet

  • Electron, Photon, Muon

  • Photon

  • Electron

  • Jets





Typical Ambiguities

  • Example 1: isolated Electron versus Jet:

    • Electron: cluster in the ECAL + pointing track in tracker

    • Calorimeter Jet: clustered energies in the ECAL + HCAL

    • The ECAL cluster will also show up reconstructed as a calorimeter jet

  • Example 2: Electron versus Photon:

    • Electron: cluster in the ECAL + pointing track in tracker

    • Photon: cluster in the ECAL + w/o track in tracker (BUT there can be conversions)

    • On the pure calorimeter level electrons show up as photons

Typical Concerns

  • Example 1: isolated Electron versus Jet:

    • Not an issue for a inclusive QCD jets analysis (only considers single object type)

    • More an issue for a ttbar + jets analysis in the semi-leptonic channel with electrons

    • Double counting of objects

    • Double counting of energy in the detector

Cross Cleaning (object disambiguation) matters, if you want to interpret parts or the whole event

A Word of Caution

Cross Cleaning and its exact configuration is a very analysis dependent procedure

  • Different aims for analyses and attitudes of PAGs :

    • QCD Jets

    • Inclusive V-Boson production

    • V+Jets

    • Top

    • Top+Jets

    • SUSY+Exotica

Concepts for Disambiguation

  • We have a reference collection and a set of test collections

    • E.g. test=Jets – reference=electrons

  • We have a matching criterion

    • E.g. geometrical matching (by DeltaR)

  • We might want to apply a pre-selections to the reference collection

    • E.g. electrons should be isolated

Cleaning in PAT


  • End of the Food Chain

  • After full PAT-ification

  • python/cleaningLayer1


Cross Cleaning in the PAT Workflow

  • Cross Cleaning is the last step in the patTuple creation chain:

    • It's the most high-level analysis operation

    • Input are the 'highest analysis objects' up to then

  • If I don't want to apply cross cleaning I just stop before

    • Or use the removeCleaning(process) tool

  • Even when cross cleaning is applied NO information and NO objects lost!

Algorithmic Procedure (PAT Default)


  • Keep as it is

  • Clean if overlap (within ∆R<0.3) withMuon

  • Clean if same SuperClusterSeed as Electrons

  • Clean if overlap (within ∆R<0.3) with Muon

  • Clean if overlap (within ∆R<0.3) with Electron

  • Clean if overlap (within ∆R<0.5) with Muon, Photon, Electron

  • Clean if overlap (within ∆R<0.3) with track-isolated Electron

  • Clean if overlap (within ∆R<0.5) with Tau





Here the order Matters:

As some cleanPatCandidateshave other cleaned collections as input!

Configuration of Electrons

edm::InputTagsrccommon input source

std::stringpreselectionpotential pre-selection

edm::PSetcheckOverlapsconfig. of overlap checking

std::stringfinalCutpotential final selection

src input source for test collection

algorithm 'bySupperClusterSeed' algorithm

preselectionpreselection for ref collection

checkReco check for common reco components

paircut apply cut on pairing (4-vector kin.)1)

requireNo...drop overlaps from this collection

1) for potential cuts in pairCut have a look at PatUtils/interface/PatDiObjectProxy.h


Checking & Handling of Overlaps


The return values are of type reco::Candidate, but as you know of what type the test collections were you can also access specific information via dynamic_cast



  • Cross Cleaning is an issue for analyses that plan to interpret whole events

  • It is very analysis dependent and people are very cautious about it

  • PAT provides a very flexible frame for a standardized way of Cross Cleaning

    • The way how to apply cross cleaning

    • Which objects to consider for cross cleaning

    • In what order to apply cross cleaning

  • There is NO LOSS of information with it (unless stated explicitly)

  • In contrary we have a gain of information (selectedPatCandidate+more)



Exercise 1:

  • cvs up ­r patTutorial_mar10_module3a PhysicsTools/PatExamples

  • Take the cleanPatJetcollection as it comes with the PAT default and analyze it with the PatBasicAnalyer. Check how many overlaps the jets have with electrons in general and with track-isolated electrons.

  • Exercise 2:

  • Create a patTuple that contains both selected &cleanPatCandidates.Modify the default behavior for jets such, that it discards jets that overlap with isolated electrons.

  • Make use of PhysicsTools/PatExamples/test/analyzePatBasics_modified_cfg.py to compare the two

Exercise 3:

  • Configure the module for jet cleaning such that it discards all jets that overlap with anyelectron or photon with a pt>20GeV. Compare the two cleaned jet collections using the PatBasicAnalyzer.