Using unsupervised learning to detect broken symmetries, with relevance to searches for parity violation in nature. (Previously: "Stressed GANs snag desserts")

TL;DR

This paper introduces a simple machine learning approach using symmetrised outputs to detect symmetry breaking, demonstrated through a toy example related to parity violation, with potential applications in particle physics and other fields.

Contribution

The paper presents a novel unsupervised learning method for detecting symmetry breaking by symmetrising model outputs, applicable to various symmetry types including discrete and continuous.

Findings

Effective detection of symmetry breaking demonstrated on toy examples.

Method applicable to both discrete and continuous symmetries.

Potential for application in particle physics and other symmetry-related fields.

Abstract

Testing whether data breaks symmetries of interest can be important to many fields. This paper describes a simple way that machine learning algorithms (whose outputs have been appropriately symmetrised) can be used to detect symmetry breaking. The original motivation for the paper was an important question in Particle Physics: "Is parity violated at the LHC in some way that no-one has anticipated?" and so we illustrate the main idea with an example strongly related to that question. However, in order that the key ideas be accessible to readers who are not particle physicists but who are interesting in symmetry breaking, we choose to illustrate the method/approach with a 'toy' example which places a simple discrete source of symmetry breaking (the handedness of human handwriting) within a idealised particle-physics-like context. Readers interested in seeing extensions to continuous symmetries, non-ideal environments or more realistic particle-physics contexts are provided with links to separate papers which delve into such details.