Artificial proto-modelling with simplified-model results from the LHC

TL;DR

This paper introduces a new method using random walk algorithms and the SModelS framework to identify potential signals of new physics in LHC results by testing proto-models against existing simplified-model data.

Contribution

It presents a novel approach combining proto-model generation and analysis selection algorithms to efficiently explore new physics signals within LHC constraints.

Findings

Successfully identifies analysis regions with potential signals

Demonstrates the method's compatibility with existing LHC data

Provides a framework for constructing likelihoods in proto-model space

Abstract

We present a novel approach to identify potential dispersed signals of new physics in the slew of published LHC results. It employs a random walk algorithm to introduce sets of new particles, dubbed "proto-models", which are tested against simplified-model results from ATLAS and CMS searches for new physics by exploiting the SModelS software framework. A combinatorial algorithm identifies the set of analyses and/or signal regions that maximally violates the Standard Model hypothesis, while remaining compatible with the entirety of LHC constraints in our database. Crucial to the method is the ability to construct a reliable likelihood in proto-model space; we explain the various approximations which are needed depending on the information available from the experiments, and how they impact the whole procedure.