Single-Trajectory Characterization of Active Swimmers in a Flow

Gaspard Junot (1) , Eric Clément (1, 2) , Harold Auradou (3) , Reinaldo García-García (1)

1 PMMH - Physique et mécanique des milieux hétérogenes (UMR 7636)
2 IUF - Institut universitaire de France
3 FAST - Fluides, automatique, systèmes thermiques

We develop a maximum likelihood method to infer relevant physical properties of elongated active particles. Using individual trajectories of advected swimmers as input, we are able to accurately determine their rotational diffusion coefficients and an effective measure of their aspect ratio, also providing reliable estimators for the uncertainties of such quantities. We validate our theoretical construction using numerically generated active trajectories upon no flow, simple shear, and Poiseuille flow, with excellent results. Being designed to rely on single-particle data, our method eases applications in experimental conditions where swimmers exhibit a strong morphological diversity. We briefly discuss some of such ongoing experimental applications, specifically, in the characterization of swimming E. coli in a flow.

Mots clés: Fluctuations & noise; Stochastic inference; Swimming; Active matter; Bacteria; Lagrangian particle tracking; Langevin algorithm; Path-integral methods; Stochastic analysis methods; Fluctuations & noise

Domaines: Biophysique [physics.bio-ph]; Mécanique des fluides [physics.class-ph]; Analyse de données, Statistiques et Probabilités [physics.data-an]

Gaspard Junot, Eric Clément, Harold Auradou, Reinaldo García-García. Single-Trajectory Characterization of Active Swimmers in a Flow. Physical Review E , 2021, 103 (3), pp.032608. ⟨10.1103/PhysRevE.103.032608⟩. ⟨hal-03267100⟩