An Approximation to the Cross Sections of Z_l Boson Production at CLIC by Using Neural Networks

TL;DR

This paper uses artificial neural networks to predict invariant mass distributions of a hypothetical Z_l boson at CLIC, providing empirical formulas that aid in studying its properties and potential signals.

Contribution

It introduces a method employing neural networks to model Z_l boson production, creating empirical formulas for complex non-linear data in collider physics.

Findings

Neural networks accurately predict muon invariant mass distributions.

Constructed empirical physical formulas from ANN outputs.

Provides a new approach for analyzing hypothetical boson signals.

Abstract

In this work, the possible dynamics associated with leptophilic Z_l boson at CLIC (Compact Linear Collider) have been investigated by using artificial neural networks (ANNs). These hypotetic massive boson Z_l have been shown through the process e+e- -> M+M-. Furthermore, the invariant mass distributions for final muons have been consistently predicted by using ANN. For these highly non-linear data, we have constructed consistent empirical physical formulas (EPFs) by appropriate feed- forward ANN. These ANN-EPFs can be used to derive further physical functions which could be relevant to studying Z_l.