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Extracting bb Higgs decay signals using multivariate techniques

Extracting bb Higgs decay signals using multivariate techniques. Clarke Smith. Outline. Higgs search at ATLAS Multivariate methods Event generation with PYTHIA Event processing with ROOT Higgs mass reconstruction with TMVA Results. Higgs search at ATLAS.

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Extracting bb Higgs decay signals using multivariate techniques

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  1. Extracting bb Higgs decay signals using multivariate techniques Clarke Smith

  2. Outline • Higgs search at ATLAS • Multivariate methods • Event generation with PYTHIA • Event processing with ROOT • Higgs mass reconstruction with TMVA • Results

  3. Higgs search at ATLAS • Higgs boson h evidence of a theoretical mechanism for giving fermions and bosons mass • mass width of several MeV • gghbb

  4. signal gghbb background ggbb

  5. In pp-collisions (events), detect resulting hadrons and measure their pT, η, and ϕ • So-called “jet combinatorics problem:” how to partition hadrons into jets to reconstruct event information • Many “mass-reconstruction algorithms” for this • all produce pT, η, and ϕ for b, b, and h • use different R values to isolate jets • mbb reconstruction plots theoretically show background with tiny, wide mh (signal) bump • Goal: observe bump by narrowing it

  6. Multivariate methods • Methods used to reconstruct mh: neural networks (NN) and boosted regression trees (BRT) • Train method by feeding it inputs and targets (true mh)for each event • Method searches for patterns in the inputs and correlations to true mh • Use outputs from 25 mass-reconstruction algorithms as inputs for NN and BRT

  7. Event generation with PYTHIA • Generate 7×105ggh bb (signal) events • Specify mh = 90, 100, 110, 120, 130, 140, 150 GeV generated event mh

  8. Event processing with ROOT • 25 mass-reconstruction algorithms applied to each event – output is input for NN/BRT variables single algorithm reconstructed mh

  9. Higgs mass reconstruction with TMVA • To run TMVA: feed data, select method(s), specify variables, and choose parameters • TMVA uses half of the sample for training and half for testing • Select variables based on effectiveness and redundancy • effective if ranked highly by TMVA method • redundant if strongly correlated to another variable • Optimize parameters with RMS comparison • NN parameters: HiddenLayers, NeuronType, NeuronInputType, etc. • BRT parameters: NTrees, BoostType, SeparationType, etc.

  10. Results • Overall, BRT with GradientBoost yielded best predictions units are MeV

  11. reconstructed mh using BRT with GradientBoost for PYTHIA-generated 120 GeV Higgs events previous mh reconstruction attempt using NN for ALPGEN-generated 120 GeV Higgs events

  12. Future work • Optimize parameters algorithmically • Generate events with more Higgs masses • Process events with more variables • Combine multivariate methods • Test on actual ATLAS data

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