ZZ Fusion Analysis

This is a detailed record on how the Marlin framework and adjacent packages are used for our analysis of the feasibility of the ZZ fusion channel with CLIC ILD at 1.4 TeV. ZZ Fusion Analysis Jet Finder and Flavour Tagging Neural Network Training Purity and Efficiency Studies Flavour Tagging Acceptance Studies Electrons from Hard Bremsstrahlung Luminosity and Event Weights

Jet Finder and Flavour Tagging

We use the LCFI flavour tagging package. This package consists of a topological vertex finder ZVTOP, which reconstructs secondary interactions, and a multivariate classifier which combines several jet-related variables to tag bottom, charm, and light quark jets (see diagram).

Our steering file will contain the jet finder, flavour tagging and LCFI processors, and we will write new slcio files containing the added collections:

  <group name="JetFinders"/>
  <group name="MyTrueAngularJetFlavourProcessorCollection"/>
  <processor name="IPRPCutProcessor"/>
  <processor name="MyPerEventIPFitterProcessor"/>
  <processor name="ZVRESRPCutProcessor"/>
  <processor name="MyZVTOP_ZVRES"/>
  <processor name="FTRPCutProcessor"/>
  <processor name="MyFlavourTagInputsProcessor"/>
  <processor name="MyLCIOOutputProcessor"/>  

The JetFinder processor reconstructs 4 jets events from the input collection (LooseSelectedPandoraPFANewPFOs was used). For the reconstructed 4 jets, MyTrueAngularJetFlavourProcessor determines MC Jet Flavour by angular matching of heavy quarks to jets, and also determines hadronic and partonic charge of the jet

The LCFI processors have the following functions:

• IPRPCut - selects Reconstructed Particles based on track parameters, number of hits etc.
• MyPerEventIPFitter - determines IP position and error from the tracks in an event by simple fitting
• ZVRESRPCut - applies cuts on the d0 and z0 values of the track
• MyZVTOP_ZVRES - topological vertex finder
• FTRPCut - flavour tagging reconstructed particle cuts (on d0, z0 and P_T)
• MyFlavourTagInputs - from vertices and tracks calculates discriminating variables for the neural net

Table of input and output collections for our setup (one can choose other names, of course):

Our input slcio files contain the collections: LooseSelectedPandoraPFANewPFOs, MCParticlesSkimmed, PandoraPFANewClusters, PandoraPFANewPFOs, PandoraPFANewReclusterMonitoring, ProngVertices, RecoMCTruthLink, SelectedLDCTracks, SelectedPandoraPFANewPFOs, TightSelectedPandoraPFANewPFOs and V0Vertices.

The LCIOOutput processor creates new slcio files containing the new collections added by the above processors. We found that the most time-consuming processor is ZVTOP_ZVRES with more than 10 s/event.

Neural Network Training

The slcio files created at the previous step contain the collections Durham_4Jets, FlavourTagInputs and TrueJetFlavour_4Jets, which we will use now to train our neural nets. We use the NeuralNetTrainer code included in the LCFI package. Separate nets were trained for 1, 2, or 3+ vertices to identify b-jets, c-jets, and c-jets with b background. Our steering file contains only:

  <processor name="MyNeuralNetTrainer" type="NeuralNetTrainer"/>

The neural nets are saved as XML files in nnets/ and will be used for flavour tagging (next step). No slcio output is written at this time.

Purity and Efficiency Studies

To determine the optimal cut for our b-tagging, a purity vs. efficiency study was performed.

Flavour Tagging

Now are ready to employ the FlavourTag processor, which will do flavour tagging using the neural nets trained in the previous step. Our steering file contains the following processors:

  <processor name="MyFlavourTag"/>
  <processor name="ZZfusion"/>

The ZZfusion processor is used in our analysis.

Acceptance Studies

Electrons from Hard Bremsstrahlung

Luminosity and Event Weights