use of multivariate analysis (mva) technique in data analysis rakshya khatiwada 08/08/2007

Hunting for Higgs

Use of Multivariate Analysis (MVA) Technique in Data Analysis

Rakshya Khatiwada08/08/2007

Index1. Background2. My work3. MVA4. MLP (Type of MVA techniques)5. Details of work6. Result7. Conclusion and Future8. Acknowledgement

Why study Higgs Boson?Higgs field is supposed to be responsible for

the mass of the elementary particles

Thus, the term Higgs boson

Standard model incomplete without Higgs (not considering Gravity!). Thus need of research on Higgs.

Current Status from CDF and D0Why is it taking so long to detect Higgs?

Not enough Luminosity to detect it. Current limit of 3fb-1 in DØ and CDF but required Luminosity is ????

My work Current : Comparison of Conventional Data Analysis

technique with Multivariate Analysis (MLP Neural Network) using DØ MC P17.

Focused on ZH channel with and backgrounds

Here, I will be discussing only background.

tt bWb

tt

Multivariate Analysis (MVA)Statistical technique used to analyze data

that involves from more than one variable.

MVA package used - Multi Layered Perceptron (MLP)

Feed forward Neural Network (NN) (flow of information in one direction)

Consists of an input layer, two hidden nodes layer and an output layer with one node (gives either signal or background)

MLP NN (Analogous to Brain)NN with two hidden layers

Input layers

Hidden layers

Output layer

Neuron

How does NN work?Works similar to human brain where there

are input and output ports and in between, the processing takes place. Weight is applied to each parameter and processing takes place accordingly. (higher weight, higher priority)

Humans learn by example, in a similar manner, ANN is configured for a specific application such as pattern recognition or data classification through learning process. Thus, it needs to be trained.

Additional information

bbbbZHpp

bbWHpp

,

Signal

Background

tbtbqWZZZbZbbWbttpp ,,,,,,

Channels

xbb

xbb

xbbET

Single lepton

Di-lepton

Missing TE

Variables usedEt

b1 - Transverse Energy of the 1st b jet

Etb2 - Transverse Energy of the 2nd b jet

Ptμ1 - Transverse Momentum of the 1st muon

Ptμ2 - Transverse Momentum of the 2nd muon

Et - Missing Transverse Energy (neutrinos)

Mbb - Mass of bb jets

Mµ+

µ- - Mass of µ+µ-

Ht - Total Transverse Energy of jets

Variable Distributi0ns

Use HT for conventional cut

Applying cuts

Calculating Signal over Root Background (SoRB)

As a function of the cut value

As a function of signal events surviving cut

Output of MLP

SoRB Comparison:As a function of the cut

MVAConventional method

SoRB Comparison:As a function of the number of signal events surviving cut

MVAConventional method

SummaryMVA gives better discrimination of Signal

and Background than conventional analysis.

Signal efficiency (S/√B) significantly higher for MVA.

Less work for us since no need to apply multiple cuts to have good discrimination.

Future planDetailed study of MLP and Bayesian NN

(definition)

Use of real data(not just MC)

Could be useful at LHC if not here for further research in Higgs.

AcknowledgementDr. Pushpa Bhat

Scientist, CMS/DØParticle Physics Division

Michael PogwizdStudent, University of Illonois Urbana

Champaign.

DØ Group

Internship for Physics MajorsFermi National Accelerator Laboratory

Jean, Roger, Erik, Carol and Fermilab family