Machine Learning Applications in High Energy Physics

The project aimed to use Supervised Machine Learning for classification of signal and background candidates of resonance particles produced in high energy collisions at the

Large Hadron Collider (LHC), CERN.

The goal was to improve the signal significance of these particles.

Toolkit for Multivariate Analysis(TMVA) was used to implement Boosted Decision
Trees(BDT) classifier in ROOT(a C++ based object-oriented software) for training, testing
and application phase of Machine Learning.

The project resulted in a better significance value by Machine Learning approach as compared to the traditional approach for Monte-Carlo generated data and well as real data obtained from the ALICE detector at the LHC, CERN.