Soft displaced leptons at the LHC

TL;DR

This paper develops a neural network-based analysis to detect soft displaced leptons at the LHC, enhancing sensitivity to compressed dark sector signals with low transverse momentum thresholds.

Contribution

It introduces a novel analysis method using neural networks for identifying low-momentum displaced leptons, improving detection prospects for weak-scale dark matter scenarios.

Findings

Weak-scale particles decaying into soft leptons can be probed with LHC Run 2 data.

Dedicated triggers are necessary to maximize sensitivity to displaced soft leptons.

The analysis achieves a comprehensive understanding of detection efficiencies and backgrounds at small momenta.

Abstract

Soft displaced leptons are representative collider signatures of compressed dark sectors with feeble couplings to the standard model. Prime targets are dark matter scenarios where co-scattering or co-annihilation sets the relic abundance upon freeze-out. At the LHC, searches for soft displaced leptons are challenged by a large background from hadron or tau lepton decays. In this article, we present an analysis tailored for displaced leptons with a low transverse momentum threshold at 20 GeV. Using a neural network, we perform a comprehensive analysis of the event kinematics, including a study of the expected detection efficiencies and backgrounds at small momenta. Our results show that weak-scale particles decaying into soft leptons with decay lengths between 1mm and 1m can be probed with LHC Run 2 data. This motivates the need for dedicated triggers that maximize the sensitivity to displaced soft leptons.