Extracting bb Higgs decay signals using multivariate techniques

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Extracting bb Higgs decay signals using multivariate techniques Clarke Smith

description

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

Transcript of Extracting bb Higgs decay signals using multivariate techniques

Page 1: Extracting  bb  Higgs decay signals using multivariate techniques

Extracting bb Higgs decay signals using multivariate techniquesClarke Smith

Page 2: Extracting  bb  Higgs decay signals using multivariate techniques

Outline

Higgs search at ATLAS

Multivariate methods

Event generation with PYTHIA

Event processing with ROOT

Higgs mass reconstruction with TMVA

Results

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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

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signalgg h bb

backgroundgg bb

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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( )

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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

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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

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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

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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

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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

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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

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Future work

Optimize parameters algorithmically

Generate events with more Higgs masses

Process events with more variables

Combine multivariate methods

Test on actual ATLAS data