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We examine motility-induced phase separation (MIPS) in two-dimensional runand tumble disk systems using both machine learning and noise fluctuationanalysis. Our measures suggest that within the MIPS state there are severaldistinct regimes as a function of density and run time, so that systems withMIPS transitions exhibit an active fluid, an active crystal, and a criticalregime. The different regimes can be detected by combining an order parameterextracted from principal component analysis with a cluster stabilitymeasurement. The principal component-derived order parameter is maximized inthe critical regime, remains low in the active fluid, and has an intermediatevalue in the active crystal regime. We demonstrate that machine learning canbetter capture dynamical properties of the MIPS regimes compared to morestandard structural measures such as the maximum cluster size. The differentregimes can also be characterized via changes in the noise power of thefluctuations in the average speed. In the critical regime, the noise powerpasses through a maximum and has a broad spectrum with a $1/f^{1.6}$ signature,similar to the noise observed near depinning transitions or for solidsundergoing plastic deformation.

Characterizing Different Motility Induced Regimes in Active Matter with Machine Learning and Noise