Space and time cluster tomography of active systems

Bacteria swarming, cell migration and the collective motion animal groups are all examples of active matter. A paradigmatic system that captures the essence of activity is the Brownian particles (ABP) model, which considers self-propelled disks with excluded volume interactions¹. ABP systems with no alignment exhibit an athermal clustering instability to a phase-separated regime. Several exciting properties such as large density fluctuations, structure factors and hexatic order parameters have been measured showing a clear contrast to equilibrium systems². Yet, as we study active systems of increasing complexity, it becomes more and more challenging to a priori identify the right order parameters. As an alternative, here we propose to perform cluster tomography in space and time by measuring the spatial gap size distribution³ and inter-event time distribution⁴ within particle clusters. We show that such measures can reliably detect different regimes and characterise the transitions between them, even without system-specific order parameters, providing a versatile tool to study a broad range of active systems.

¹Fily, et al. PRL (2012).
²Digregorio et al. PRL (2018).
³Kovács, et al. PRB (2014).
⁴Goh, et al. EPL (2008).