Deep Learning Searches for Vector-Like Leptons at the LHC and Electron/Muon Colliders

TL;DR

This paper evaluates the potential to discover vector-like leptons at the LHC and future colliders, using deep learning for analysis, and finds that leptonic colliders can probe higher masses than the LHC, especially for doublet VLLs.

Contribution

It introduces a deep learning approach for significance estimation and compares discovery prospects of VLLs at different collider types, highlighting the advantages of leptonic colliders.

Findings

Doublet VLLs can be probed up to 1 TeV at the LHC.

Singlet VLLs have limited testability beyond a few hundred GeV at the LHC.

Leptonic colliders can explore higher mass regimes for VLLs than the LHC.

Abstract

The discovery potential of both singlet and doublet vector-like leptons (VLLs) at the Large Hadron Collider (LHC) as well as at the not-so-far future muon and electron machines is explored. The focus is on a single production channel for LHC direct searches while double production signatures are proposed for the leptonic colliders. A Deep Learning algorithm to determine the discovery (or exclusion) statistical significance at the LHC is employed. While doublet VLLs can be probed up to masses of 1 TeV, their singlet counterparts have very low cross sections and can hardly be tested beyond a few hundreds of GeV at the LHC. This motivates a physics-case analysis in the context of leptonic colliders where one obtains larger cross sections in VLL double production channels, allowing to probe higher mass regimes otherwise inaccessible even to the LHC high-luminosity upgrade.