# Log-normal Superstatistics Reveals Statistical Resilience in the Panic Response of Confined Ants

## TL;DR

This study uncovers how ant groups under panic exhibit Log-normal Superstatistics, demonstrating collective resilience through a superstatistical Langevin model that captures non-Gaussian behaviors and stable cluster formation.

## Contribution

It introduces a superstatistical Langevin model explaining collective ant behavior under panic, linking Log-normal fluctuations to interaction-driven processes in confined active matter.

## Key findings

- Ant collective motion shows non-Gaussian velocity distributions.
- A superstatistical Langevin model reproduces the transition to escape response.
- Stable clusters form despite individual memory limitations.

## Abstract

We report the emergence of Log-normal Superstatistics in the collective motion of ants confined in a quasi-2D arena and exposed to a panic-inducing stimulus. A data-driven superstatistical Langevin model accurately reproduces the transition from stationary behavior to an organized escape response, characterized by non-Gaussian velocity distributions and a stochastic diffusion coefficient. Our findings show that danger information propagates via a memory-limited, cascade-like mechanism, resulting in a stable cluster formation despite individual memory constraints. These results indicate that a slowly varying diffusivity arises from the multiplicative combination of interaction-mediated processes under confinement, leading naturally to Log-normal fluctuations. The persistence of this statistical structure under panic reveals a form of collective resilience, establishing a mechanistic bridge between Superstatistics and living active matter in confined environments.