Detecting subtle macroscopic changes in a finite temperature classical scalar field with machine learning
This provides a proof-of-concept for using AI to detect subtle changes in many-body systems, which could benefit experimental physics, but it is incremental as it builds on existing methods in a toy model.
The study tackled the problem of detecting subtle macroscopic changes in many-body systems, such as scalar fields at varying temperatures, by comparing physics, statistics, and AI methods, finding that the AI method outperformed the others in sensitivity.
The ability to detect macroscopic changes is important for probing the behaviors of experimental many-body systems from the classical to the quantum realm. Although abrupt changes near phase boundaries can easily be detected, subtle macroscopic changes are much more difficult to detect as the changes can be obscured by noise. In this study, as a toy model for detecting subtle macroscopic changes in many-body systems, we try to differentiate scalar field samples at varying temperatures. We compare different methods for making such differentiations, from physics method, statistics method, to AI method. Our finding suggests that the AI method outperforms both the statistical method and the physics method in its sensitivity. Our result provides a proof-of-concept that AI can potentially detect macroscopic changes in many-body systems that elude physical measures.