Data driven background determination for SUSY searches with ATLAS

TL;DR

This paper discusses data-driven methods developed for the ATLAS experiment at the LHC to accurately estimate Standard Model backgrounds, crucial for discovering or excluding TeV-scale SUSY signals.

Contribution

It introduces novel data-driven background estimation techniques tailored for SUSY searches with the ATLAS detector.

Findings

Effective background estimation improves SUSY detection sensitivity.

Data-driven methods reduce reliance on potentially inaccurate Monte Carlo simulations.

Enhanced background understanding aids in robust SUSY exclusion or discovery.

Abstract

Supersymmetry (SUSY) is an attractive extension of the Standard Model possibly solving many standing issues in particle physics and cosmology. The general purpose ATLAS detector at the Large Hadron Collider (LHC) is an experiment capable of discovering or excluding TeV SUSY. However discovery can only be claimed when the Standard Model backgrounds are understood and are under control. The expectations at the LHC are that Monte Carlo simulation predictions may not be sufficient to achieve this and the backgrounds will have to determined from data itself. In this note we will highlight some data driven methods developed to estimate backgrounds and detect a possible SUSY excess.