Reduce, Reuse, Reinterpret: an end-to-end pipeline for recycling particle physics results

TL;DR

This paper presents an end-to-end pipeline that leverages public LHC analysis resources and simulation tools to reinterpret existing collider search results for new, untested physics models with high accuracy.

Contribution

It introduces a comprehensive, automated reinterpretation pipeline combining public analysis data with simulation tools, enabling accurate constraints on new physics models beyond original benchmarks.

Findings

Successfully constrained new SUSY models with compressed sleptons and electroweakinos.

Demonstrated high accuracy in reinterpretation compared to original analyses.

Provided an open-source Python package for community use.

Abstract

Searches for new physics at the Large Hadron Collider have constrained many models of physics beyond the Standard Model. Many searches also provide resources that allow them to be reinterpreted in the context of other models. We describe a reinterpretation pipeline that examines previously untested models of new physics using supplementary information from ATLAS Supersymmetry (SUSY) searches in a way that provides accurate constraints even for models that differ meaningfully from the benchmark models of the original analysis. The public analysis information, such as public analysis routines and serialized probability models, is combined with common event generation and simulation toolkits MadGraph, Pythia8, and Delphes into workflows steered by TOML configuration files, and bundled into the mapyde python package. The use of mapyde is demonstrated by constraining previously untested SUSY models with compressed sleptons and electroweakinos using ATLAS results.