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Signal Enhancement Using Multivariate Classification Techniques and Physical Constraints

Signal Enhancement Using Multivariate Classification Techniques and Physical Constraints. Ricardo Vilalta Puneet Sarda Gordon Mutchler Paul Padley. Outline. Introduction The Goal Kinematic Fitting Experiments. Introduction. Multivariate Classification Techniques Bayesian Functions

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Signal Enhancement Using Multivariate Classification Techniques and Physical Constraints

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  1. Signal Enhancement Using Multivariate Classification Techniques and Physical Constraints Ricardo Vilalta Puneet Sarda Gordon Mutchler Paul Padley

  2. Outline • Introduction • The Goal • Kinematic Fitting • Experiments

  3. Introduction • Multivariate Classification Techniques • Bayesian Functions • Neural Networks • Decision Trees • Rule based • Experiments using CLAS • Detecting charged particles, inferring uncharged • Measure momentum, polar angle and azimuthal angle, time of flight • Infer mass • Using G1C dataset

  4. Introduction • K*+ measurement is not the real interest. • We use it as a convenient test case to develop the multivariate techniques which will be used on new data. Reactions we will look for Background Reactions

  5. The Goal • Empirical comparison of several multivariate classification techniques for signal enhancement • Use of Kinematic Fitting to enhance original feature representation • Effect of cost matrices in generalization performance

  6. Kinematic Fitting • Mathematical procedure • Takes advantage of constraints such as energy/momentum conservation • improve measured quantities • provide a means to cut background

  7. Experiments • WEKA • Characteristics of Data • Feature Selection • Initial Classification • Cost Sensitive Classification

  8. Characteristics of Data • 1000 MC Signal Samples • ~6000 MC Background Samples • ~13,500 Real Samples • 45 Attributes • Attribute 1 – 4 : Confidence Levels • Attribute 5 : Total Energy Level • Attribute 6 – 44 : (3 measured + 5 derived) Four Vectors + Mass**2 • Attribute 45 : Class (Signal/Background)

  9. Feature Selection • Analysis using Information Gain • Comparison of top 5/10/15 attributes • Final selection = top 5

  10. Initial Classification

  11. Cost Sensitive Classification

  12. Comparison

  13. Random Forest • Grows many classification trees • Voting among trees • Growing a tree • Sampling on N cases • M input variables • No pruning • Error rate

  14. Histograms

  15. Histograms

  16. Histograms

  17. Histograms

  18. Summary • Monte Carlo Data • Kinematic Fitting • Learning Algorithm • Real Data • Signal Enhancement

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