Simulation-based Anomaly Detection for Multileptons at the LHC

TL;DR

This paper explores machine learning-based, model-agnostic anomaly detection methods for multilepton final states at the LHC, aiming to enhance sensitivity to a wide range of potential new physics signals beyond current dedicated searches.

Contribution

It introduces machine learning techniques for model-agnostic searches in multilepton final states, broadening the scope beyond traditional model-specific analyses.

Findings

Machine learning methods provide broad coverage across parameter space.

Performance loss compared to dedicated searches is about one order of magnitude.

Methods can detect signals with no existing dedicated search at the LHC.

Abstract

Decays of Higgs boson-like particles into multileptons is a well-motivated process for investigating physics beyond the Standard Model (SM). A unique feature of this final state is the precision with which the SM is known. As a result, simulations are used directly to estimate the background. Current searches consider specific models and typically focus on those with a single free parameter to simplify the analysis and interpretation. In this paper, we explore recent proposals for signal model agnostic searches using machine learning in the multilepton final state. These tools can be used to simultaneously search for many models, some of which have no dedicated search at the Large Hadron Collider. We find that the machine learning methods offer broad coverage across parameter space beyond where current searches are sensitive, with a necessary loss of performance compared to dedicated searches by only about one order of magnitude.