Search for Beyond the Standard Model physics with anomaly detection in multilepton final states in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector

TL;DR

This paper conducts a model-agnostic search for new physics beyond the Standard Model in multilepton final states using unsupervised machine learning on ATLAS Run 2 data, setting limits on various benchmark models.

Contribution

It introduces a novel, model-agnostic approach employing unsupervised machine learning to identify anomalies in multilepton events at the LHC.

Findings

No significant excess observed over Standard Model background.

Limits set on vector-like leptons, charginos, neutralinos, and smuons.

First limits established on the flavourful vector-like lepton model.

Abstract

A model-agnostic search for Beyond the Standard Model physics is presented, targeting final states with at least four light leptons (electrons or muons). The search regions are separated by event topology and unsupervised machine learning is used to identify anomalous events in the full 140 fb$^{-1}$ of proton-proton collision data collected with the ATLAS detector during Run 2. No significant excess above the Standard Model background expectation is observed. Model-agnostic limits are presented in each topology, along with limits on several benchmark models including vector-like leptons, wino-like charginos and neutralinos, or smuons. Limits are set on the flavourful vector-like lepton model for the first time.