arXiv subjects

Thibault Bertrand

Publications and source records attributed to Thibault Bertrand.

At least 19 recordsLinked to original sources

arXiv2026

Run-and-tumble particles with preferred reorientation

Run-and-tumble particles (RTPs) are canonically modeled with uniform reorientation probabilities, an assumption that breaks down for many biological microswimmers. In this work, we investigate the dynamics of RTPs with arbitrary non-uniform tumble distributions. By deriving an exact Doi-Peliti field theory, we explicitly calculate a wide array of spatial and orientational observables. Notably, we demonstrate that the spatial dynamics exhibit an effective persistence and chirality governed entirely by the first Fourier modes of the tumble distribution, establishing a formal mapping to the dynamics of chiral active Brownian particles. Furthermore, our field-theoretic framework provides a systematic method to compute spatial moments to arbitrary order, allowing for the complete characterization and identification of complex tumbling dynamics. We illustrate the framework with wrapped Gaussian and bimodal Gaussian distributions, demonstrating explicit control over persistence and chirality. We further extend the field theory to $d$ dimensions, recovering the mean squared displacement in terms of a single effective tumble rate. Our results establish a direct link between the shape of the tumble distribution and the emergent dynamics, and provide a foundation for the study of interacting RTPs with non-uniform reorientation.

cond-mat.stat-mech↗

arXiv2026

Finite-Time Optimal Control by Noisy Traps

The optimal control of passive systems in equilibrium typically favours quasistatic (infinite-time) protocols. We show that a breakdown of quasistatic optimality occurs when the controller itself is dissipative. Concretely, we study a Brownian particle confined by a harmonic trap with stochastically fluctuating stiffness, driven by an external protocol. When these fluctuations violate detailed balance, the probe-controller coupling continuously exchanges work with the system, altering the optimisation landscape. In this regime, optimal protocols are characterised by a finite duration which vanishes above a critical fluctuation strength. This transition can be directly observed in a short-time expansion of the mean work functional. When imposing an endpoint constraint, the transition to zero duration disappears and finite duration protocols remain optimal for all values of the controller fluctuations. These results demonstrate that finite-time optimality can emerge in passive systems under nonequilibrium control.

cond-mat.stat-mech↗

arXiv2025

Kinetic theory of pattern formation in a generalized multi-species Vicsek model

The theoretical understanding of pattern formation in active systems remains a central problem of interest. Heterogeneous flocks made up of multiple species can exhibit a remarkable diversity of collective states that cannot be obtained from single-species models. In this paper, we derive a kinetic theory for multispecies systems of self-propelled particles with (anti)alignment interactions. We summarize the numerical results for the binary system before employing linear stability analysis on the coarse-grained system. We find good agreement between theoretical predictions and particle simulations, and our kinetic theory is able to capture the correct lengthscale in the emergent coexistence phases through a Turing-Hopf instability. Extending the kinetic framework to multispecies systems with cyclic alignment interactions, we recover precisely the same emergent ordering as corresponding simulations of the microscopic model. More generally, our kinetic theory provides an extensible framework for analyzing pattern formation and collective order in multispecies active matter systems.

cond-mat.stat-mech↗

arXiv2025

Controlling the Glass Transition through Active Fluctuating Interactions

Fluctuating pairwise interactions are understood to drive fluid-like states in dense biological systems. These states find a broad range of functionalities, such as directing growth during morphogenesis and forming aggregates with heightened mechanical response. However, a tractable model capturing the role of microscopic fluctuating interactions in these structural transitions is crucially lacking. Here, we study a $p$-spin model with fluctuating pairwise couplings (of strength $D_a$ and persistence time $t_a$) as a schematic model for interaction-mediated fluidization. We find that while stronger fluctuations suppress the glass transition, more persistent fluctuations have the opposite effect. We identify the presence of an emergent fluctuation-dissipation relation at long times. We numerically extract the critical temperature $T_c(D_a, t_a)$ from a scaling relation near the transition, illustrating how microscopic fluctuations control the glass transition.

cond-mat.stat-mech↗

arXiv2025

Stochastic Forces Enhance Tracer Diffusion in Non-motile Active Matter

Stochasticity is a defining feature of the pairwise forces governing interactions in biological systems-from molecular motors to cell-cell adhesion-yet its consequences on large-scale dynamics remain poorly understood. Here, we show that reciprocal but randomly fluctuating interactions between particles create active suspensions which can enhance the diffusion of an external tracer particle, even in the absence of self-propulsion or non-reciprocity. Starting from a lattice model with pairwise dynamics that minimally break detailed balance, we derive a coarse-grained dynamical theory for spatio-temporal density fluctuations and reveal an elevated effective temperature at short wavelengths. We then compute the self-diffusion coefficient of a tracer particle weakly coupled to our active fluid, demonstrating that purely reciprocal stochastic interactions provide a distinct and generic route to enhanced diffusivity in dense non-equilibrium suspensions.

cond-mat.soft↗

arXiv2025

Conditional splitting probabilities for hidden-state inference in drift-diffusive processes

Splitting probabilities quantify the likelihood of particular outcomes out of a set of mutually-exclusive possibilities for stochastic processes and play a central role in first-passage problems. For two-dimensional Markov processes $\{X(t),Y(t)\}_{t\in T}$, a joint analogue of the splitting probabilities can be defined, which captures the likelihood that the variable $X(t)$, having been initialised at $x_0 \in \mathbb{L}$, exits $\mathbb{L}$ for the first time via either of the interval boundaries \emph{and} that the variable $Y(t)$, initialised at $y_0$, is given by $y_{\rm exit}$ at the time of exit. We compute such joint splitting probabilities for two classes of processes: processes where $X(t)$ is Brownian motion and $Y(t)$ is a decoupled internal state, and unidirectionally coupled processes where $X(t)$ is drift-diffusive and depends on $Y(t)$, while $Y(t)$ evolves independently. For the first class we obtain generic expressions in terms of the eigensystem of the Fokker-Planck operator for the $Y$ dynamics, while for the second we carry out explicit derivations for three paradigmatic cases (run-and-tumble motion, diffusion in an intermittent piecewise-linear potential and diffusion with stochastic resetting). Drawing on Bayes' theorem, we subsequently introduce the related notion of conditional splitting probabilities, defined as the posterior likelihoods of the internal state $Y$ \emph{given} that the observable degree of freedom $X$ has undergone a specific exit event. After computing these conditional splitting probabilities, we propose a simple scheme that leverages them to partially infer the assumedly hidden state $Y(t)$ from point-wise detection events.

math-ph↗

arXiv2025

Looking Back: Field theory of transiently chiral active particles

We derive a Doi-Peliti Field Theory for transiently chiral active particles in two dimensions, that is, active Brownian particles that undergo tumbles via a diffusing reorientation angle. Using this framework, we compute the mean squared displacement for both uniformly distributed and fixed initial reorientations. We also calculate an array of orientation-based observables, to quantify the transiently chiral behaviour observed.

cond-mat.stat-mech↗

arXiv2025

Flocking Beyond One Species: Novel Phase Coexistence in a Generalized Two-Species Vicsek Model

A hallmark in natural systems, self-organization often stems from very simple interaction rules between individual agents. While single-species self-propelled particle systems are well understood, the behavior of binary mixtures with general alignment interactions remains largely unexplored with a few scattered results hinting at the existence of a rich emergent phase behavior. Here, we investigate systematically a generalization of the two-species Vicsek model with reciprocal intra- and interspecies (anti)alignment couplings, uncovering a rich phenomenology of emergent states. Notably, we show that rather than destroying polar order, antialigning interactions can promote phase separation and the emergence of global polar order. In doing so, we uncover a novel mechanism for microphase separation. We further find these coexistence patterns can be generalized to multispecies systems with cyclic alignment interactions.

cond-mat.soft↗

arXiv2025

Eph-ephrin-mediated differential persistence as a mechanism for cell sorting

The phenomenon of cell sorting/segregation, by which cells organise spatially into clusters of specific cell type or function, is essential for tissue morphogenesis. This self-organization process involves an interplay between mechanical, biochemical, and cellular mechanisms that act across various spatial and temporal scales. Several mechanisms for cell sorting have been proposed; however, the physical nature of these mechanisms and how they lead to symmetric or asymmetric cell sorting remains unclear. Here, using experimental data from cocultures of genetically modified Human Embryonic Kidney (HEK293) cells and numerical simulations, we show the existence of a cell sorting mechanism based on transient increases in the persistence of motion of cells. This mechanism is activated on cells overexpressing the ephrinB1-related receptor EphB2 after their interaction with cells overexpressing ephrinB1. We show that this mechanism is sufficient to cause cell sorting, breaking the symmetry of the sorting dynamics, and show that the duration of this transient differential persistence state is optimal for enhancing sorting. Furthermore, we show that in combination with other interaction mechanisms, such as changes in direction-also known as contact inhibition of locomotion-and adhesion forces, differential persistence significantly reduces the timescale of sorting. Our findings offer insights into the behaviour of cell mixtures, the relevance of non-reciprocal interactions, and may provide insight into developmental processes and tissue patterning.

physics.bio-ph↗

arXiv2025

Nested Stochastic Resetting: Nonequilibrium Steady-states and Exact Correlations

Stochastic resetting breaks detailed balance and drives the formation of nonequilibrium steady states . Here, we consider a chain of diffusive processes $x_i(t)$ that interact unilaterally: at random time intervals, the process $x_n$ stochastically resets to the instantaneous value of $x_{n-1}$. We derive analytically the steady-state statistics of these nested stochastic resetting processes including the stationary distribution for each process as well as its moments. We are also able to calculate exactly the steady-state two-point correlations $\langle x_n x_{n+j}\rangle$ between processes by mapping the problem to one of the ordering statistics of random counting processes. Understanding statistics and correlations in many-particle nonequilibrium systems remains a formidable challenge and our results provide an example of such tractable correlations. We expect this framework will both help build a model-independent framework for random processes with unilateral interactions and find immediate applications, e.g. in the modelling of lossy information propagation.

cond-mat.stat-mech↗

arXiv2024

Disordered Yet Directed: The Emergence of Polar Flocks with Disordered Interactions

Flocking is a prime example of how robust collective behavior can emerge from simple interaction rules. The flocking transition has been studied extensively since the inception of the original Vicsek model. Here, we introduce a self-propelled particle model with quenched disorder in the pairwise alignment interaction couplings akin to a spin-glass model. We find that increasing the variance of the coupling distribution can promote (rather than destroy) the emergence of global polar order. In particular, we show that our model can display a flocking phase even when the majority of the interaction couplings are antialigning. Activity is the key ingredient to reduce frustration in the system as it allows local particle clustering combined with self-organization of the particles to favor neighborhoods with strong cooperative interactions.

cond-mat.soft↗

arXiv2024

The OU$^2$ process: Characterising dissipative confinement in noisy traps

The Ornstein-Uhlenbeck (OU) process describes the dynamics of Brownian particles in a confining harmonic potential, thereby constituting the paradigmatic model of overdamped, mean-reverting Langevin dynamics. Despite its widespread applicability, this model falls short when describing physical systems where the confining potential is itself subjected to stochastic fluctuations. However, such stochastic fluctuations generically emerge in numerous situations, including in the context of colloidal manipulation by optical tweezers, leading to inherently out-of-equilibrium trapped dynamics. To explore the consequences of stochasticity at this level, we introduce a natural extension of the OU process, in which the stiffness of the harmonic potential is itself subjected to OU-like fluctuations. We call this model the OU$^2$ process. We examine its statistical, dynamic, and thermodynamic properties through a combination of analytical and numerical methods. Importantly, we show that the probability density for the particle position presents power-law tails, in contrast to the Gaussian decay of the standard OU process. In turn, this causes the trapping behavior, extreme value statistics, first passage statistics, and entropy production of the OU$^2$ process to differ qualitatively from their standard OU counterpart. Due to the wide applicability of the standard OU process and of the proposed OU$^2$ generalisation, our study sheds light on the peculiar properties of stochastic dynamics in random potentials and lays the foundation for the refined analysis of the dynamics and thermodynamics of numerous experimental systems.

cond-mat.stat-mech↗

arXiv2024

Boosting macroscopic diffusion with local resetting

Stochastic interactions generically enhance self-diffusivity in living and biological systems, e.g. optimizing navigation strategies and controlling material properties of cellular tissues and bacterial aggregates. Despite this, the physical mechanisms underlying this nonequilibrium behavior are poorly understood. Here, we introduce a model of interactions between an agent and its environment in the form of a local stochastic resetting mechanism, in which the agent's position is set to the nearest of a predetermined array of sites with a fixed rate. We derive analytic results for the self-diffusion coefficient, showing explicitly that this mechanism enhances diffusivity. Strikingly, we show analytically that this enhancement is optimized by regular arrays of resetting sites. Altogether, our results ultimately provide the conditions for the optimization of the macroscopic transport properties of diffusive systems with local random binding interactions.

cond-mat.soft↗

arXiv2023

Active Jamming at Criticality

Jamming is ubiquitous in disordered systems, but the critical behavior of jammed solids subjected to active forces or thermal fluctuations remains elusive. In particular, while passive athermal jamming remains mean-field-like in two and three dimensions, diverse active matter systems exhibit anomalous scaling behavior in all physical dimensions. It is therefore natural to ask whether activity leads to anomalous scaling in jammed systems. Here, we use numerical and analytical methods to study systems of active, soft, frictionless spheres in two dimensions, and elucidate the universal scaling behavior that relates the excess coordination, active forces or temperature, and pressure close to the athermal jammed point. We show that active forces and thermal effects around the critical jammed state can again be captured by a mean-field picture, thus highlighting the distinct and crucial role of amorphous structure in active matter systems.

cond-mat.soft↗

arXiv2023

Irreversibility across a nonreciprocal ${\cal PT}$-symmetry-breaking phase transition

Nonreciprocal interactions are commonplace in continuum-level descriptions of both biological and synthetic active matter, yet studies addressing their implications for time-reversibility have so far been limited to microscopic models. Here, we derive a general expression for the average rate of informational entropy production in the most generic mixture of conserved phase fields with nonreciprocal couplings and additive conservative noise. For the particular case of a binary system with Cahn-Hilliard dynamics augmented by nonreciprocal cross-diffusion terms, we observe a non-trivial scaling of the entropy production rate across a parity-time symmetry breaking phase transition. We derive a closed-form analytic expression in the weak-noise regime for the entropy production rate due to the emergence of a macroscopic dynamic phase, showing it can be written in terms of the global polar order parameter, a measure of parity-time symmetry breaking.

cond-mat.stat-mech↗

arXiv2023

Active Bound States Arise From Transiently Nonreciprocal Pair Interactions

Static nonreciprocal forces between particles generically drive persistent motion reminiscent of self-propulsion. Here, we demonstrate that reciprocity-breaking fluctuations about a reciprocal mean coupling strength are sufficient to generate this behavior in a minimal two-particle model, with the velocity of the ensuing \textit{active} bound state being modulated in time according to the nature of these fluctuations. To characterize the ensuing nonequilibrium dynamics, we derive exact results for the time-dependent center of mass mean-squared displacement and average rate of entropy production for two simple examples of discrete- and continuous-state fluctuations. We find that the resulting dimer can exhibit unbiased persistent motion akin to that of an active particle, leading to a significantly enhanced effective diffusivity.

cond-mat.soft↗

arXiv2023

Machine learning topological defects in confluent tissues

Active nematics is an emerging paradigm for characterising biological systems. One aspect of particularly intense focus is the role active nematic defects play in these systems, as they have been found to mediate a growing number of biological processes. Accurately detecting and classifying these defects in biological systems is, therefore, of vital importance to improving our understanding of such processes. While robust methods for defect detection exist for systems of elongated constituents, other systems, such as epithelial layers, are not well suited to such methods. Here, we address this problem by developing a convolutional neural network to detect and classify nematic defects in confluent cell layers. Crucially, our method is readily implementable on experimental images of cell layers and is specifically designed to be suitable for cells that are not rod-shaped. We demonstrate that our machine learning model outperforms current defect detection techniques and that this manifests itself in our method requiring less data to accurately capture defect properties. This could drastically improve the accuracy of experimental data interpretation whilst also reducing costs, advancing the study of nematic defects in biological systems.

cond-mat.soft↗

arXiv2023

Modeling growing confluent tissues using a lattice Boltzmann method: interface stability and fluctuations

Tissue growth underpins a wide array of biological and developmental processes, and numerical modeling of growing systems has been shown to be a useful tool for understanding these processes. However, the phenomena that can be captured are often limited by the size of systems that can be modeled. Here, we address this limitation by introducing a Lattice-Boltzmann method (LBM) for a growing system that is able to efficiently model hydrodynamic length-scales. The model incorporates a novel approach to describing the growing front of a tissue, which we use to investigate the dynamics of the interface of growing model tissues. We find that the interface grows with scaling in agreement with the Kardar-Parisi-Zhang (KPZ) universality class when growth in the system is bulk driven. Interestingly, we also find the emergence of a previously unreported hydrodynamic instability when proliferation is restricted to the tissue edge. We then develop an analytical theory to show that the instability arises due to a coupling between the number of cells actively proliferating and the position of the interface.

cond-mat.soft↗