Data-driven Model-independent Searches for Long-lived Particles at the LHC

Understand

Neutral long-lived particles (LLPs) are highly motivated by many BSM scenarios, such as theories of supersymmetry, baryogenesis, and neutral naturalness, and present both tremendous discovery opportunities and experimental challenges for the LHC.

• A major bottleneck for current LLP searches is the prediction of SM backgrounds, which are often impossible to simulate accurately.
• In this paper, we propose a general strategy for obtaining differential, data-driven background estimates in LLP searches, thereby notably extending the range of LLP masses and lifetimes that can be discovered at the LHC.
• We focus on LLPs decaying in the ATLAS Muon System, where triggers providing both signal and control samples are available at the LHC Run-2.