A method to challenge symmetries in data with self-supervised learning

Understand

Symmetries are key properties of physical models and of experimental designs, but any proposed symmetry may or may not be realized in nature.

• In this paper, we introduce a practical and general method to test such suspected symmetries in data, with minimal external input.
• Self-supervision, which derives learning objectives from data without external labelling, is used to train models to predict 'which is real?' between real data and symmetrically transformed alternatives.
• If these models make successful predictions in independent tests, then they challenge the targeted symmetries.