# Presentation: 683951

## Artificial IntelligenceParallel Sessions II, III & IV

- Harrison B. Prosper
- Rapporteur
- Florida State University

## Outline

- Introduction
- Highlights
- Comments and Conclusions

## Introduction

- Sessions II, III, IV
- 9 NN talks
- Selecting WW (DELPHI)
- Searching for single top (DØ)
- Measuring the top mass
- Searching for the Higgs
- Calorimeter energy estimation (ATLAS)
- Electron/jet discrimination (ATLAS)
- Particle identification (DØ)
- Tracking in vertex detector (HERA-B)
- Vertex finding (ZEUS)
- 4 other talks: Job scheduling, confidence bounds, rule induction, kernel-based discrimination

## Selecting WW events in DELPHI(Müller)

- Goal
- To measure WW  4parton cross-section
- Signal: e+e W+W  4 jets
- Backgrounds: e+e  Z0/  or Z0 Z0  4 jets
- Method
- (13,7,1) feed-forward neural network
- Trained using stochastic minimization (back-propagation)
- Results
- Selection efficiency times purity increases from 63.5%  (using conventional 1-d cuts) to 69% (NN)

## Search for Single Top (DØ)(Lev Dudko)

- Goal
- To extract a signal starting with S/B ratio of ~ 1:500
- 5 backgrounds
- 2 processes
- 2 decay modes
- Method
- Construct a tree of networks trained using the program MLPfit 1.4

## Search for Single Top (DØ) (cont)

- S/B ~ 1/23 after NN cuts
- Yellow band: model uncertainty
- Triangles: observed distribution using
- independent data-set not used in training

## Measuring the Top Mass(Suman B. Beri)

- Goal
- Reduce uncertainty in the top quark mass to << 5 GeV
- Run II promises ~ 100 x more data relative to Run I
- Smaller statistical errors
- Therefore, need smaller systematic errors also.
- Studies
- Explore use of robust variables in NNs

## Measuring the Top Mass (cont)

- Find variables that are less sensitive to systematic effects such as the jet energy scale.
- For example:
- x1 = f(e,b1)
- x2 = f(,b2)
- x3 = f(e,b2)
- x4 = f(,b1)
- 	where
- Combine into a single mass-dependent function:
- Y = network(x1,x2 ,x3,x4)

- End-point occurs at the top mass if b quark
- and the lepton are correctly paired

## Measuring the Top Mass (cont, 2)

- Restriction to finite range [100,250] GeV causes distortions at low and high mass
- Need way to correct for distortions
- One idea: use NN output distributions
- P(y|(mt)
- in Bayes’ Theorem
- and take mass estimate to be
- 	arg max[P(m|y)]

## Finding the Higgs at the Tevatron (Silvia Tentindo-Repond)

- Studies in the Run II Susy/Higgs Workshop have shown that NN methods can effect a significant reduction (~ 2) in the required integrated luminosity to discover a Higgs boson with mass < 130 GeV.
- NN show great promise for channel-independent b-tagging
- Preliminary results suggest they may be useful in improving the di-jet invariant mass
- Studies are underway to test ideas about channel-dependent b-tagging and to systematically search for better event variables for use with NNs and other multivariate methods.

## Calorimeter Energy Estimation in ATLAS(J. Seixas)

- Goal
- To reduce the (10.6%) non-linearity in calorimeter response while maintaining acceptable energy resolution (E ~ 50%E)
- Construct a network-based energy estimation function using test-beam data.
- Method
- Training done in two stages:
- In first stage NN target outputs set to beam energy
- In second stage target outputs set to linear sum of calorimeter cells.
- Results
- Non-linearity < 2.8% and (E ~ 54%E)

## Electron/Jet Discrimination in ATLAS(J. Seixas)

- Goal
- To discriminate between electrons and jets
- Method
- Exploit segmentation of calorimeter and pre-process data to reduce dimensionality from 496 cells. Extract the most important features from each segment by a principal component analysis (PCA)
- The “n” PCA components form the input to a (n,33,1) network
- Results
- With 33 components (<< 496), obtain 99% electron efficiency and < 1% false electron efficiency.

## Particle identification at DØ(Dhiman Chakraborty)

- Goal
- To improve significantly on the electron and tau identification with respect to that available during Run I, which was based on Fisher discriminants constructed from 41x41 matrices.
- Method
- Train a (7,16,1) network with features that discriminate between electrons and jets.
- Results
- Factor ~ 10 improvement in S/B ratio for electrons and ~2 for taus.

## Vertex Reconstruction in ZEUS(Erez Etzion)

- Goal

- Keep these events

- Reject these!

## NN Layer 1 (large retina with ~ 100,000 neurons)
receptive fields  of neurons give (x,y) position of hits
NN Layer 2 (segment finder)
Gives segment orientation.
NN Layer 3 (arc finder)
Gives arc (ring)
NN Final layer (track finder)

- receptive fields  of neurons give (x,y) position of hits
- NN Layer 2 (segment finder)
- Gives segment orientation.
- NN Layer 3 (arc finder)
- Gives arc (ring)
- NN Final layer (track finder)

- Vertex Reconstruction in ZEUS (cont) Hierarchical clustering

## Rule Induction(Nikita Stepanov)

- Goal
- To create a system that can report how it has arrived at its decision. The output of such a system is basically a decision tree.
- Method
- Manipulate (and evolve) symbolic structures
- Test case (MC study)
- Improving b-tagging in pp  ttH  4b + lepton events
- Results
- Tagging efficiency per jet ~ 62%

## Looking for Instantons at Hera(B. Koblitz)

- Goal
- Separate instanton events from deeply inelastic scattering events. Problem: S/B starts at about ~ 1/100. Need fast way to experiment with many sets of variables.
- Method
- Classify each point (event) using Kernel density estimation. Key idea is to use a t ~ nlog(n) range searching algorithm to find points in the neighborhood of each event and to classify the event according to the value of the discriminant D.

## Looking for Instantons at Hera (cont)

- Results
- At least as good as NN, but growing tree is much faster than training a network with comparable performance
- 20 mins vs 4 hours on a Linux system!

## Comments and Conclusions

- All classification methods are approximations to the same problem: the calculation of class posterior probabilities: Pr(class|Data)
- Experience suggests that no method is uniformly the best over the set of all possible problems. Therefore, one should embrace them all.
- That being said, however, it is useful to have a default method and it seems that neural networks are emerging as the method of choice for classification and functional approximation.
- It has been amply demonstrated at this workshop, in the many excellent talks, that neural networks work very well in practice. But it is necessary to exercise a fair measure of non-artificial intelligence to render that statement true!