Use of Machine Learning Technique to maximize the signal over background for $H \rightarrow \tau \tau$

TL;DR

This paper explores the application of artificial neural networks to enhance the detection of Higgs boson decays into tau leptons by improving signal-to-background classification in particle physics data.

Contribution

It introduces a machine learning approach, specifically neural networks, to optimize the identification of Higgs to tau tau events, advancing analysis techniques in high-energy physics.

Findings

Neural networks improve signal detection efficiency.

Enhanced classification accuracy over traditional methods.

Potential for more precise Higgs boson measurements.

Abstract

In recent years, artificial neural networks (ANNs) have won numerous contests in pattern recognition and machine learning. ANNS have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers, and gene prediction. Here, we intend to maximize the chances of finding the Higgs boson decays to two $\tau$ leptons in the pseudo dataset using a Machine Learning technique to classify the recorded events as signal or background.