Hunting for vampires and other unlikely forms of parity violation at the Large Hadron Collider

TL;DR

This paper introduces a model-independent, data-driven method using machine learning to detect any form of parity violation at the LHC, potentially uncovering new physics beyond the Standard Model.

Contribution

It presents a novel, model-independent approach to search for parity violation at the LHC using machine learning to construct sensitive parity-odd variables.

Findings

Method successfully detects parity violation in simulated data.

Approach is model-independent and sensitive to all forms of parity violation.

Data and software are publicly available for further research.

Abstract

Non-Standard-Model parity violation may be occurring in LHC collisions. Any such violation would go unseen, however, as searches are for it are not currently performed. One barrier to searches for parity violation is the lack of model-independent methods sensitive to all of its forms. We remove this barrier by demonstrating an effective and model-independent way to search for parity-violating physics at the LHC. The method is data-driven and makes no reference to any particular parity-violating model. Instead, it inspects data to construct sensitive parity-odd event variables (using machine learning tools), and uses these variables to test for parity asymmetry in independent data. We demonstrate the efficacy of this method by testing it on data simulated from the Standard Model and from a non-standard parity-violating model. This result enables the possibility of investigating a variety of previously unexplored forms of parity violation in particle physics. Data and software are shared at https://zenodo.org/record/6827724