Discoveries far from the Lamppost with Matrix Elements and Ranking

TL;DR

This paper develops model-independent analysis techniques for LHC searches, utilizing matrix element methods and likelihood-based background flattening to improve sensitivity to unknown signals.

Contribution

It introduces new procedures for adapting the Matrix Element Method and background flattening techniques for model-independent searches at the LHC.

Findings

Enhanced background suppression using likelihood-based flattening.

Three novel methods for event ranking and reweighting.

Improved sensitivity in null result searches.

Abstract

The prevalence of null results in searches for new physics at the LHC motivates the effort to make these searches as model-independent as possible. We describe procedures for adapting the Matrix Element Method for situations where the signal hypothesis is not known a priori. We also present general and intuitive approaches for performing analyses and presenting results, which involve the flattening of background distributions using likelihood information. The first flattening method involves ranking events by background matrix element, the second involves quantile binning with respect to likelihood (and other) variables, and the third method involves reweighting histograms by the inverse of the background distribution.