Artificial Proto-Modelling: Building Precursors of a Next Standard Model from Simplified Model Results

TL;DR

This paper introduces a novel algorithm that systematically searches for potential new physics signals in LHC results by generating and testing 'proto-models' against existing simplified-model constraints, identifying promising new physics candidates.

Contribution

The paper presents a new random walk-based algorithm to generate and evaluate proto-models against LHC data, enabling the discovery of potential new physics signals without prior assumptions.

Findings

Identified a proto-model with a top partner, light-flavor quark partner, and neutral particle around 1.2 TeV, 700 GeV, and 160 GeV.

Achieved a global p-value of approximately 0.19 for the SM hypothesis, indicating potential deviations.

Demonstrated the method's effectiveness using the SModelS database and existing LHC results.

Abstract

We present a novel algorithm 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 (exploiting the SModelS software framework). A combinatorial algorithm identifies the set of analyses and/or signal regions that maximally violates the SM hypothesis, while remaining compatible with the entirety of LHC constraints in our database. Demonstrating our method by running over the experimental results in the SModelS database, we find as currently best-performing proto-model a top partner, a light-flavor quark partner, and a lightest neutral new particle with masses of the order of 1.2 TeV, 700 GeV and 160 GeV, respectively. The corresponding global p-value for the SM hypothesis is approximately 0.19; by construction no look-elsewhere effect applies.