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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. - PowerPoint PPT Presentation
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Extracting bb Higgs decay signals using multivariate techniquesClarke 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
Higgs boson h evidence of a theoretical mechanism for giving fermions and bosons mass mass width of
several MeV
gg h bb
signalgg h bb
backgroundgg bb
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
η =−ln tanθ
2⎛⎝⎜
⎞⎠⎟
R = φ,η( )
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
Event generation with PYTHIA Generate 7×105 gg h bb (signal) events
Specify mh = 90, 100, 110, 120, 130, 140, 150 GeV
generated event mh
Event processing with ROOT 25 mass-reconstruction algorithms applied to
each event – output is input for NN/BRT
pT ,ii∑ηb
ηb
pT ,b
pT ,b
ΔRbb
ηh
pT ,h
mh
variables
single algorithm reconstructed mh
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.
RMS = mh,regression −mh,true( )2
Results
Overall, BRT with GradientBoost yielded best predictions
method RMS mean deviation
truncated RMS
truncated mean deviation
NN 1.25×104 47 9.73×103 1.21×103
BRT with AdaBoost.R2
1.64×104 956 1.58×104 1.49×103
BRT with GradientBoost
1.24×104 -1.49×103 8.99×103 -281
units are MeV
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
Future work
Optimize parameters algorithmically
Generate events with more Higgs masses
Process events with more variables
Combine multivariate methods
Test on actual ATLAS data