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

Understand

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.