Recasting LHC searches for long-lived particles with MadAnalysis 5

TL;DR

This paper extends MadAnalysis 5's SFS framework to simulate long-lived particles in high-energy physics detectors, enabling more accurate recasting of LHC searches for such particles using Python and C++.

Contribution

It introduces new methods in MadAnalysis 5 for simulating long-lived particles, including magnetic field effects, and validates implementations of three LHC analyses.

Findings

Successfully implemented three LHC analyses for long-lived particles

Validated the new simulation methods against existing analyses

Made the implementations publicly available for community use

Abstract

We present an extension of the simplified fast detector simulator of MadAnalysis 5 - the SFS framework - with methods making it suitable for the treatment of long-lived particles of any kind. This allows users to make use of intuitive Python commands and straightforward C++ methods to introduce detector effects relevant for long-lived particles, and to implement selection cuts and plots related to their properties. In particular, the impact of the magnetic field inside a typical high-energy physics detector on the trajectories of any charged object can now be easily simulated. As an illustration of the capabilities of this new development, we implement three existing LHC analyses dedicated to long-lived objects, namely a CMS run 2 search for displaced leptons in the $e\mu$ channel (CMS-EXO-16-022), the full run 2 CMS search for disappearing track signatures (CMS-EXO-19-010), and the partial run 2 ATLAS search for displaced vertices featuring a pair of oppositely-charged leptons (ATLAS-SUSY-2017-04). We document the careful validation of all MadAnalysis 5 SFS implementations of these analyses, which are publicly available as entries in the MadAnalysis 5 Public Analysis Database and its associated dataverse.