Search for a Dark Gauge Boson Within Einstein-Cartan Theory at the ILC Using Multivariate Analysis

TL;DR

This study uses multivariate analysis techniques to search for a dark gauge boson predicted by Einstein-Cartan theory at the ILC, demonstrating most benchmark points are discoverable with 500 fb$^{-1}$ of data.

Contribution

It applies advanced MVA classifiers to simulate and analyze the potential discovery of a dark gauge boson within Einstein-Cartan theory at the ILC.

Findings

Most benchmark points are discoverable at the ILC with 500 fb$^{-1}$.

Deep Neural Network classifiers effectively discriminate signal from background.

The muonic decay channel provides promising detection prospects.

Abstract

Multivariate analysis (MVA) is employed to probe the dark matter candidate A$^{\prime}$, a gauge boson of a model rooted within Einstein-Cartan Theory, at the International Linear Collider (ILC). \texttt{WHIZARD} package is used as the event generator to simulate electron-positron interactions at the ILC, at a 500 GeV center-of-mass energy ($\sqrt s$) and a 500 fb$^{-1}$ detector's integrated luminosity ($\mathcal{L}$), to produce the A$^{\prime}$ signal and the expected standard model background. The study focuses on the muonic decay channel of A$^{\prime}$, utilizing several MVA classifiers such as Fisher, Deep Neural Network (DNN), and the Boosted Decision Tree (BDT), aiming to discriminate between the signal of several benchmark points, within theoretical and experimental limits, and the standard model background, and to explore their discovery potential at the ILC. Most benchmark points were proved discoverable at the ILC within $\mathcal{L}= 500$ fb$^{-1}$.