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The ability to detect macroscopic changes is important for probing thebehaviors of experimental many-body systems from the classical to the quantumrealm. Although abrupt changes near phase boundaries can easily be detected,subtle macroscopic changes are much more difficult to detect as the changes canbe obscured by noise. In this study, as a toy model for detecting subtlemacroscopic changes in many-body systems, we try to differentiate scalar fieldsamples at varying temperatures. We compare different methods for making suchdifferentiations, from physics method, statistics method, to AI method. Ourfinding suggests that the AI method outperforms both the statistical method andthe physics method in its sensitivity. Our result provides a proof-of-conceptthat AI can potentially detect macroscopic changes in many-body systems thatelude physical measures.

Detecting subtle macroscopic changes in a finite temperature classical scalar field with machine learning