Don't be left in the dark: Improving LHC searches for dark photons using lepton-jet substructure

TL;DR

This paper enhances LHC search techniques for dark photons by applying lepton-jet substructure analysis and machine learning, significantly improving detection prospects for electron-only dark photon decays.

Contribution

It introduces a novel method combining lepton-jet substructure variables with boosted decision trees to better distinguish dark photon signals from background noise.

Findings

Improved background rejection for electron lepton jets.

Enhanced LHC sensitivity to dark photons in specific models.

Effective use of machine learning in collider physics analysis.

Abstract

Collider signals of dark photons are an exciting probe for new gauge forces and are characterized by events with boosted lepton jets. Existing techniques are efficient in searching for muonic lepton jets but due to substantial backgrounds have difficulty constraining lepton jets containing only electrons. This is unfortunate since upcoming intensity frontier experiments are sensitive to dark photon masses which only allow electron decays. Analyzing a recently proposed model of kinetic mixing, with new scalar particles decaying into dark photons, we find that existing techniques for electron jets can be substantially improved. We show that using lepton-jet-substructure variables, in association with a boosted decision tree, improves background rejection, significantly increasing the LHC's reach for dark photons in this region of parameter space.