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Hunting for Higgs

Hunting for Higgs. Use of Multivariate Analysis (MVA) Technique in Data Analysis. Rakshya Khatiwada 08/08/2007. Index. Background My work MVA MLP (Type of MVA techniques) Details of work Result Conclusion and Future Acknowledgement. Why study Higgs Boson?.

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Hunting for Higgs

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  1. Hunting for Higgs Use of Multivariate Analysis (MVA) Technique in Data Analysis Rakshya Khatiwada 08/08/2007

  2. Index • Background • My work • MVA • MLP (Type of MVA techniques) • Details of work • Result • Conclusion and Future • Acknowledgement

  3. 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.

  4. Current Status from CDF and D0 • Why 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 ????

  5. 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.

  6. 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)

  7. MLP NN (Analogous to Brain) • NN with two hidden layers Output layer Input layers Neuron Hidden layers

  8. 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.

  9. Additional information Signal Background Single lepton Channels Di-lepton Missing

  10. Variables used • Etb1 - Transverse Energy of the 1st b jet • Etb2 - Transverse Energy of the 2nd b jet • Ptμ1 - Transverse Momentum of the 1stmuon • Ptμ2 - Transverse Momentum of the 2ndmuon • Et - Missing Transverse Energy (neutrinos) • Mbb - Mass of bb jets • Mµ+µ- - Mass of µ+µ- • Ht - Total Transverse Energy of jets

  11. Variable Distributi0ns Use HT for conventional cut

  12. Applying cuts

  13. Calculating Signal over Root Background (SoRB) As a function of the cut value As a function of signal events surviving cut

  14. Output of MLP

  15. SoRB Comparison:As a function of the cut Conventional method MVA

  16. SoRB Comparison:As a function of the number of signal events surviving cut Conventional method MVA

  17. Summary • MVA 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.

  18. Future plan • Detailed 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.

  19. Acknowledgement Dr. Pushpa Bhat Scientist, CMS/DØParticlePhysics Division Michael Pogwizd Student, University of Illonois Urbana Champaign. DØ Group

  20. Internship for Physics Majors Fermi National Accelerator Laboratory Jean, Roger, Erik, Carol and Fermilab family

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