Lepton Flavour Violation Identification in Tau Decay ($\tau^{-} \rightarrow \mu^{-}\mu^{-}\mu^{+}$) Using Artificial Intelligence

TL;DR

This paper evaluates various AI algorithms, including neural networks and gradient boosting machines, for identifying lepton flavor violation signals in tau decays using simulated collider data, highlighting the effectiveness of certain deep learning models.

Contribution

It presents a comprehensive comparison of custom neural networks and traditional ML algorithms for LFV detection, an area with limited recent exploration.

Findings

XGBoost and 10-layer Dense Block Neural Network achieved AUC of 0.88.

Deep learning models showed promising results, outperforming some traditional algorithms.

LightGBM failed the CvM test, indicating limitations in certain models.

Abstract

The discovery of neutrino oscillation, proving that neutrinos do have masses, reveals the misfits of particles in the current Standard Model (SM) theory. In theory, neutrinos having masses could result in lepton flavour not being a symmetry called Lepton Flavour Violation (LFV). While SM theory extensions allowed LFV processes, their branching fractions are too small, making them unobservable even with the strongest equipment up-to-date. With that, scientists in recent years have generated LFV-like processes from the combined LHCb and Monte-Carlo-Simulated data in an attempt to identify LFV using Artificial Intelligence (AI), specifically Machine Learning (ML) and Deep Learning (DL). In this paper, the performance of several algorithms in AI has been presented, such as XGBoost, LightGBM, custom 1-D Dense Block Neural Networks (DBNNs), and custom 1-D Convolutional Neural Networks (CNNs) in identifying LFV signals, specifically $\tau^{-} \rightarrow \mu^{-}\mu^{-}\mu^{+}$ decay from the combined LHCb and Monte-Carlo-Simulated data that imitates the signatures of the said decay. Kolmogorov-Smirnov (KS) and Cramer-von Mises (CvM) tests were also conducted to verify the validity of predictions for each of the trained algorithms. The result shows decent performances among algorithms, except for the LightGBM, for failing the CvM test, and a 20-layered CNN for having recorded a considerably low AUC. Meanwhile, XGBoost and a 10-layered DBNN recorded the highest AUC of 0.88. The main contribution of this paper is the extensive experiment involving custom DBNN and CNN algorithms in different layers, all of which have been rarely used in the past years in identifying LFV-like signatures, unlike GBMs and tree-based algorithms, which have been more popular in the said task.