Dynamically emergent correlations in Brownian particles subject to simultaneous non-Poissonian resetting protocols (2509.06658v1)

Abstract: We consider a one-dimensional gas of $N$ independent Brownian particles subject to simultaneous stochastic resetting, with inter-reset times drawn from a general waiting-time distribution $\psi(\tau)$. This includes the well-known Poissonian case, where $\psi(\tau)=re^{-r\tau}$, and extends to more general classes of resetting, such as heavy-tailed and bounded distributions. We show that the simultaneous resetting generates correlations between particles dynamically. These correlations grow with time and eventually drive the system into a strongly correlated non-equilibrium stationary state (NESS). Exploiting the renewal structure of the resetting dynamics, we derive explicit analytical expressions for the joint distribution of the positions of the particles in the NESS. We show that the NESS has a conditionally independent and identically distributed (CIID) structure that enables us to compute various physical observables exactly for arbitrary $\psi(\tau)$. These observables include the average density, extreme value and order statistics, the spacing distribution between consecutive particles and the full counting statistics, i.e., the distribution of the number of particles in a given interval centered at the origin. We discuss the universal features of the large $N$ scaling behaviors of these observables for different choices of the resetting protocol $\psi(\tau)$. Our results provide an interesting example of a stochastic control whereby, by tuning the inter-reset distribution $\psi(\tau)$, one can generate a class of tunable, and yet solvable, strongly correlated NESS in a many-body system.