Difference: JetFlavourTagging (4 vs. 5)

Revision 52013-07-03 - DanProtopopescu

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META TOPICPARENT	name="WebHome"

ZZ Fusion Analysis

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Changed:

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< The JetFinder processor reconstructs 4 jets events from the input collection (LooseSelectedPandoraPFANewPFOs was used). For the reconstructed 4 jets, TrueAngularJetFlavourProcessor does 'truth tagging', i.e. determines the MC jet flavour (b-jet or c-jet).

>
> 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.

Changed:

<
<

PerEventIPFitter - finds the event Interaction Point
ZVRESRP - vertex finder for reconstructed particles
ZVTOP_ZVRES - topological vertex finder
FTRPCut: - flavour tagging reconstructed particle cuts
FlavourTagInputs - from vertices and tracks calculates discriminating variables for the neural net

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>

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):

Processor	Type	Input Collection name	Output Collection name
JetFinder	SatoruJetFinder	LooseSelectedPandoraPFANewPFOs	Durham_4Jets
MyTrueAngularJetFlavour	TrueAngularJetFlavour	MCParticlesSkimmed, Durham_4Jets	TrueJetFlavour_4Jets
IPRPCut	RPCut	LooseSelectedPandoraPFANewPFOs	IPFitSelectedParticles
MyPerEventIPFitter	PerEventIPFitter	IPFitSelectedParticles	IPVertex
ZVRESRPCut	RPCut	RecoMCTruthLink, Durham_4Jets	ZVRESSelectedJets
MyZVTOP_ZVRES	ZVTOP_ZVRES	IPVertex, ZVRESSelectedJets	ZVRESDecayChains, ZVRESDecayChainRPTracks, ZVRESSelectedJets
FTRPCut	RPCut	RecoMCTruthLink, ZVRESDecayChains	FTSelectedJets
MyFlavourTagInputs	FlavourTagInputs	ZVRESDecayChains, FTSelectedJets	FlavourTagInputs

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

Changed:

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< 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:

>
> The slcio files created at the previous step contain the collections FTSelectedJets, 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"/>

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