1. Implementing Boosting and Convolutional Neural Networks For Particle Identification (PID)
Khalid Teli .

2. Need for Advanced PID
Upgrade in ALICE. The upgrade will increase the rate of particle collisions improving the quality of results.
Artificial neural networks are used to identify particles at ALICE. A similar experiment MiniBooNE uses boosting to identify particles.
ALICE’s upgrade is imposing new challenges, that require the development of a current particle identification algorithms.
Need to determine what type of machine learning algorithms are needed for PID, after upgrade.
Technique: All we need is to supress more background and keep high signal efficiency
information left by a particle passing through a particle detector to identify the type of particle.

3. To investigate the boosting algorithms for decision trees and Convolutional Neural Networks, for the purpose of improving particle identification, by defining criteria of separation between signal and background.
Aim:

4. Objectives
1) ALICE type Monte Carlo data will be used in the first instance. Experimentally collected data at CERN will subsequently be used when available.
2) To determine the suitable particle identification algorithm, among a number of available boosting algorithms ,by studying the goodness of separation between signal and background.
3) To assess the ability for Convolutional Neural Network as stand-alone or in conjunction with the boosting , to enhance the particle identification.

5. Current PID techniques
Boosting
From the set of boosting algorithms tested, AdaBoost was determined to be the best performing boosting algorithm
Boosting has performance benefits over artificial neural networks, when the number of predictors is large.
Artificial Neural Networks
Artificial neural networks with a single hidden layer using the sigmoid activation function performed best.
At ALICE there are only six predictors per particle.
This nullifies the benefit that boosting algorithms have over artificial neural networks

6. Why Convolution Neural Networks?
Core problem in HEP is correct categorization of the particle interactions, recorded in detectors as signals and background.
In High Energy Physics , computer vision has made great advance by extraction of the features, using a machine learning algorithm, known as convolution neural network.
Optimal image representation for each computer vision task is unique, and finding the optimal deep CNN structure for extracting that representation, is a challenge.

7. Motivation for combining Boosting with CNNs in PID+
-Powerful image representation:
Convolution Neural Networks
-Ability of boosting to combine multiple learners, to make strong classifier
-Algorithm to incorporate boosting weights into the CNN.
-Boosting: Real time face detection
In objectives…

8. Programming Languages
Python
Matlab R

9. Conclusion
1) Use MC data sets to test efficiency of boosting algorithms
2) Using Boosting with CNN
3) Test and compare different flavours of boosting , to see which one works best with CNN
4) idea to combine CNN and Boosting is because of the powerful image representation of CNN and the ability of Boosting to optimise weak classifiers
5) This should result in an improved PID
This project will look for a more efficient particle identification technique.

10. Thank you for your attention. Any Questions?