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 ZZ Fusion Analysis | |||||||||||
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| < < | Neural Network training | ||||||||||
| > > | Jet Finder and Flavour Tagging | ||||||||||
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| < < | We use the flavour tagging package LCFIVertex. 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. | ||||||||||
| > > | 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). | ||||||||||
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| < < | Our steering file will contain the following LCFI component processors and a neural net trainer: | ||||||||||
| > > | Our steering file will contain the jet finder, flavour tagging and LCFI processors, and we will write new slciofiles containing the added collections: | ||||||||||
| <group name="JetFinders"/> <group name="MyTrueAngularJetFlavourProcessorCollection"/> | |||||||||||
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| < < | These processors have the following functions: | ||||||||||
| > > | 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 LCFI processors have the following functions: | ||||||||||
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| > > | The LCIOOutput processor creates new slciofiles 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 TrainingTheslciofiles 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 theNeuralNetTrainercode 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). Noslciooutput is written at this time. | |||||||||||
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| > > | Purity and Efficiency StudiesTo determine the optimal cut for our b-tagging, a purity vs. efficiency study was performed. | ||||||||||
| Flavour Tagging | |||||||||||
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| > > | 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: | ||||||||||
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| < < | A new file containing with these collections added is saved to be used in our analysis. | ||||||||||
| > > | <processor name="MyFlavourTag"/> <processor name="ZZfusion"/> | ||||||||||
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| < < | Acceptance Studies | ||||||||||
| > > | The ZZfusion processor is used in our analysis. | ||||||||||
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| < < | Four Jet Events | ||||||||||
| > > | Acceptance Studies | ||||||||||
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| < < | Purity and Efficiency Studies | ||||||||||
| Electrons from Hard BremsstrahlungLuminosity and Event Weights | |||||||||||
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| < < | -- DanProtopopescu - 2013-06-28 | ||||||||||
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  Copyright © 2008-2025 by the contributing authors. All material on this collaboration platform is the property of the contributing authors.
Copyright © 2008-2025 by the contributing authors. All material on this collaboration platform is the property of the contributing authors.