Machine-Learning Application for a Likelihood Ratio Estimation Problem at LHC

Abstract

High-energy physics is now entering a new era. Current particle experiments, such as ATLAS at the Large Hadron Collider at CERN, offer the possibility of discovering new and interesting physics’ phenomena beyond the Standard Model, the theoretical framework which describes fundamental interactions between particles. In this paper a machine-learning algorithm to deal with the estimate of the expected background processes distribution, a very demanding task for particle physics analyses, is described. A new technique is exploited, inspired by the well-known likelihood ratio estimation problem, called direct importance estimation in statistics. First, its theoretical formulation is discussed, then its performances in two ATLAS analyses are described.

Keywords

• experimental particle physics
• machine learning
• background estimation
• likelihood ratio estimation
• LHC
• ATLAS experiment