Use of Machine Learning Technique to maximize the signal over background for $H \rightarrow ττ$
This work addresses the challenge of particle detection in high-energy physics, but it appears incremental as it applies existing methods to a specific dataset without claiming major breakthroughs.
The paper tackled the problem of identifying Higgs boson decays to two tau leptons by applying a machine learning technique to classify events as signal or background, aiming to maximize the signal-to-background ratio in a pseudo dataset.
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 $τ$ leptons in the pseudo dataset using a Machine Learning technique to classify the recorded events as signal or background.