Hemisphere Mixing: a Fully Data-Driven Model of QCD Multijet Backgrounds for LHC Searches

TL;DR

This paper introduces a data-driven hemisphere mixing method to accurately model QCD multijet backgrounds in LHC searches, improving background estimation by generating realistic artificial events from experimental data.

Contribution

The paper presents a novel hemisphere mixing technique that constructs QCD background models directly from data, enhancing precision and robustness in LHC multijet background estimation.

Findings

The method accurately reproduces the kinematics of QCD multijet events.

It remains insensitive to small signal contaminations.

Performance tests show improved background modeling in LHC searches.

Abstract

A novel method is proposed here to precisely model the multi-dimensional features of QCD multi-jet events in hadron collisions. The method relies on the schematization of high-pT QCD processes as 2->2 reactions made complex by sub-leading effects. The construction of libraries of hemispheres from experimental data and the definition of a suitable nearest-neighbor-based association map allow for the generation of artificial events that reproduce with surprising accuracy the kinematics of the QCD component of original data, while remaining insensitive to small signal contaminations. The method is succinctly described and its performance is tested in the case of the search for the hh->bbbb process at the LHC.