The analysis of complex physical systems hinges on the ability to extract relevant degrees freedom from among many others. Though much hope is placed in machine learning, it also brings challenges, chief which interpretability. It often unclear what relation, if any, architecture- and training-dependent learned "relevant" features bear standard objects theory. Here we report theoretical results...