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Maximizing Higgs Decay Signals through Multivariate Techniques

Explore how multivariate methods enhance Higgs boson signal extraction, mass reconstruction, and future research prospects using various algorithms and event processing tools.

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Maximizing Higgs Decay Signals through 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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