arXiv subjects

Ralf Metzler

Publications and source records attributed to Ralf Metzler.

At least 19 recordsLinked to original sources

arXiv2026

Improved mean squared displacement analysis for anomalous single particle trajectories

The mean squared displacement (MSD) is a cornerstone in the analysis of diffusion processes in complex media. When the system is heterogeneous and, in particular, when single-particle trajectories are short, it is essential to extract maximal information from each measured trajectory. This is typically done by time-averaging squared increments and examining the scaling of the time-averaged MSD in log-log space. However, classical regression methods perform poorly in this setting because time-averaging introduces correlations aggravated by those inherent to anomalous diffusion. We tackle these limitations by applying a generalized least squares framework, which substantially reduces variance and bias in diffusion parameter estimates, especially for short (around 100 points) and ultra-short (around 10 points) trajectories. The method is fully automated and requires no supervision. Furthermore, it enables prediction of estimation error probability density, which is asymptotically Gaussian, for both classical and enhanced approaches. Leveraging this prediction, we introduce a specialized deconvolution algorithm that reconstructs the underlying particle ensemble structure from experimental data.

cond-mat.stat-mech↗

arXiv2026

Poisson-shot-noise hybrid machines: efficiency and quasistatic divergence

We study stochastic models of a microscopic active heat engine, comprised of an overdamped Brownian particle trapped in a harmonic potential, and in simultaneous contact with thermal (passive) and athermal (active) baths. The interaction with the active bath is modeled as a stochastic force described by Poisson shot-noise (PSN) having a specified amplitude distribution. With analytical calculations and numerical simulations, we study the thermodynamic performance of the machine to quasistatic cyclic protocols analogous to those running two-stroke and Stirling-like engines. For specific parameter ranges, the thermodynamic behavior is that of a $\textit{hybrid machine}$, simultaneously operating as a heat engine with respect to the passive/active baths and as a refrigerator with respect to the passive/active baths. Focusing on the parameter region where the overall performance is such of an engine, we show that the average total extracted work per cycle divided by average total heat intake from the cold baths per cycle may surpass the Carnot efficiency associated with the temperature of the passive baths. Applying the second law for active heat engines, we focus on a bona fide efficiency (bounded by Carnot's efficiency) that incorporates an information-theoretic metric $\mathcal{I}-$ which we call $\textit{quasistatic divergence}-$ quantifying how distinguishable are the engine's statistics in the quasistatic limit with respect to a continually changing equilibrium distribution. We analyze, with theory and numerical simulations, how the PSN shot rate and the degree of non-Gaussianity in the particle position distribution influence the efficiency of the engine, and explore the correlation between non-Gaussianity and efficiency. Our findings reveal optimal PSN shot rates maximizing the engine's efficiency and an intriguing non-bijective relation between efficiency and kurtosis

cond-mat.stat-mech↗

arXiv2026

Mean-field theory of myopic self-avoiding fractional Brownian motion

Myopic self-avoiding fractional Brownian motion (FBM) is a stochastic process in which an ensemble of particles is driven by fractional Gaussian noise while being repelled by the gradient of the time-integrated ensemble density [J. House, R. Bakhshizada, S. Janu\v{s}onis, R. Metzler, and T. Vojta, Phys. Rev. E 112, 034119 (2025)]. Depending on the anomalous diffusion exponent $\alpha$ characterizing the noise, the process features two dynamical regimes: an interaction-dominated regime ($\alpha < \alpha_c=4/(d+2)$) where the mean-density interaction governs long-time dynamics, and a noise-dominated regime ($\alpha > \alpha_c$) where FBM correlations prevail. In the interaction-dominated regime, the mean-squared displacement grows as $\langle r^2(t) \rangle \sim t^{4/(d+2)}$ regardless of $\alpha$, while for $\alpha > \alpha_c$ the standard FBM scaling $\langle r^2(t) \rangle \sim t^{\alpha}$ is recovered. Here, we develop an analytical mean-field theory of myopic self-avoiding FBM, based on a Fokker-Planck approach to the interaction-dominated regime. This allows us to derive closed-form polynomial solutions for the probability density. To compare with computer simulations, we develop an efficient radial binning algorithm that significantly reduces the computational complexity, making large-scale three-dimensional simulations feasible. Extensive simulations in one, two, and three dimensions confirm the analytical predictions. We also discuss the application of the process to the self-organization of serotonergic axons (fibers) in vertebrate brains, where FBM paths with self-avoidance provide a natural framework for understanding spatial heterogeneities of fiber densities.

cond-mat.stat-mech↗

arXiv2026

Anomalous statistics in the Langevin equation with fluctuating diffusivity: from Brownian yet non-Gaussian diffusion to anomalous diffusion and ergodicity breaking

Diffusive motion is a fundamental transport mechanism in physical and biological systems, governing dynamics across a wide range of scales -- from molecular transport to animal foraging. In many complex systems, however, diffusion deviates from classical Brownian behaviour, exhibiting striking phenomena such as Brownian yet non-Gaussian diffusion (BYNGD) and anomalous diffusion. BYNGD describes a frequently observed statistical feature characterised by the coexistence of linear mean-square displacement (MSD) and non-Gaussian displacement distributions. Anomalous diffusion, in contrast, involves a nonlinear time dependence of the MSD and often reflects mechanisms such as trapping, viscoelasticity, heterogeneity, or active processes. Both phenomena challenge the conventional framework based on constant diffusivity and Gaussian statistics. This review focuses the theoretical modelling of such behaviour via the Langevin equation with fluctuating diffusivity (LEFD) -- a flexible stochastic framework that captures essential features of diffusion in heterogeneous media. LEFD not only accounts for BYNGD but also naturally encompasses a wide range of anomalous transport phenomena, including subdiffusion, ageing, and weak ergodicity breaking. Ergodicity is discussed in terms of the correspondence between time and ensemble averages, as well as the trajectory-to-trajectory variability of time-averaged observables. The review further highlights the empirical relevance of LEFD and related models in explaining diverse experimental observations and underscores their value to uncovering the physical mechanisms governing transport in complex systems.

cond-mat.stat-mech↗

arXiv2026

An Extended Model of Non-Integer-Dimensional Space for Anisotropic Solids with q-Deformed Derivatives

We propose a non-integer-dimensional spatial model for anisotropic solids by incorporating a q-deformed derivative operator, inspired by the Tsallis nonadditive entropy framework. This generalization provides an analytical framework to explore anisotropic thermal properties, within a unified and flexible mathematical formalism. We derive explicit expressions for the phonon density of states and specific heat capacity, highlighting the impact of the deformation parameter q on the thermodynamic behavior. We apply the model to various solid-state materials, achieving excellent agreement with experimental data across a wide temperature range, and demonstrating its effectiveness in capturing anisotropic and subextensive effects in real systems. Beyond providing accurate fits, we anchor the q-deformation in a microscopic disorder/kinetics exponent μemerging from conformable dynamics, thereby linking nonextensive statistics to measurable heterogeneity and memory effects.

cond-mat.stat-mech↗

arXiv2026

Genuine and spurious (non-)ergodicity in single particle tracking

In single-particle tracking experiments measuring anomalous diffusion dynamics, understanding ergodicity is crucial, as it ensures that the time average of an observable matches the ensemble average, and can thus be fitted with known ensemble-averaged observables. A commonly used criterion for assessing the ergodicity of a stochastic process is based on the comparison of the mean-squared displacement (MSD) with the time-averaged MSD (TAMSD). This approach has been widely applied and proves effective in cases of weak ergodicity breaking across various systems in both theoretical and experimental studies. However, there is relatively little discussion regarding the theoretical justification and limitations of this definition. Here, we demonstrate that this widely accepted criterion to some extent contradicts the classical definition of ergodicity as well as physical intuition, leading to spurious (non-)ergodicity results when applied to several well-known stochastic models. To address this limitation, we propose using the mean-squared increment (MSI) instead of the MSD for comparison of ensemble- and time-averaged observables. Several well-established examples demonstrate that our MSI-TAMSD criterion not only effectively reveals weak ergodicity breaking, equivalent to the MSD-TAMSD approach, but also provides a more accurate characterization of the genuine (non-)ergodicity of systems where the MSD-TAMSD method fails. Additionally, for systems exhibiting "ultraweak" ergodicity breaking, the MSI can reveal the asymptotic stationarity and ergodic nature of the process' increments. Our findings emphasize the important role of the MSI observable for SPT experiments and anomalous diffusion studies.

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arXiv2026

Critical dynamics govern the evolution of political regimes

The emergence and decline of democratic systems worldwide raises fundamental questions about the dynamics of political change. Contrary to the idea of a stable endpoint of liberal democracy, recent backsliding towards less democratic regimes highlights the non-stationary nature of regime evolution. Here, we analyse the historical trajectories of countries within a two-dimensional regime space derived from the principal components of the Varieties of Democracy dataset. We observe weakly non-ergodic dynamics unfolding in an effective landscape characterised by sparse and shifting basins of stability. Step sizes and sojourn times characterising this dynamics follow heavy-tailed distributions near the critical regime, in which mean values appear to diverge. These facts point to the intermittent and heterogeneous nature of the regime change dynamics. A continuous time random walk model reproduces the dynamics of the three most recent decades with remarkable accuracy. Together, these results suggest that some aspects of political regime evolution follow universal stochastic principles, while remaining punctuated by unique historical pathways.

cond-mat.stat-mech↗

arXiv2026

A mobility based approach to transport in chiral fluids

Chiral fluids, for which the mobility tensor has antisymmetric, off-diagonal components, exhibit transport phenomena absent in conventional systems, including interaction-enhanced diffusion and negative mobility. While these effects have been predicted theoretically and observed in simulations, their microscopic origin has remained unclear. Here, we address this question using a mobility-based nonequilibrium approach, analysing the steady-state drift of a tracer driven through an interacting chiral fluid. We show that, under strong chirality, the tracer generates a reversed density wake, in which regions of particle accumulation and depletion are inverted compared to the achiral case. This structural inversion of the wake provides a unified physical mechanism underlying both enhanced diffusion and negative mobility. Furthermore, we demonstrate that these phenomena are robust to changes in the interaction potential, highlighting their generality as a consequence of odd mobility.

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arXiv2026

Fastest first-passage time for multiple searchers with finite speed

We study analytically and numerically the mean fastest first-passage time (fFPT) to an immobile target for an ensemble of $N$ independent finite-speed random searchers driven by dichotomous noise and described by the telegrapher's equation. In stark contrast to the well-studied case of Brownian particles -- for which the mean fFPT vanishes logarithmically with $N$ -- we uncover that the mean fFPT is bounded from below by the minimal ballistic travel time, with an exponentially fast convergence to this bound as $N \to \infty$. This behavior reveals a dramatic efficiency advantage of physically realistic, finite-speed searchers over Brownian ones and illustrates how diffusive macroscopic models may be conceptually misleading in predicting the short-time behavior of a physical system. We extend our analysis to anomalous diffusion generated by Riemann-Liouville-type dichotomous noises and find that target detection is more efficient in the superdiffusive regime, followed by normal and then subdiffusive regimes, in agreement with physical intuition and contrary to earlier predictions.

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arXiv2026

Conformable Scaling and Critical Dynamics: A Unified Framework for Phase Transitions

We investigate the application of conformable derivatives to model critical phenomena near continuous phase transitions. By incorporating a deformation parameter into the differential structure, we derive unified expressions for thermodynamic observables such as heat capacity, magnetization, susceptibility, and coherence length, each exhibiting power-law behavior near the critical temperature. The conformable derivative framework naturally embeds scale invariance and critical slowing down into the dynamics without resorting to fully nonlocal fractional calculus. Modified Ginzburg-Landau equations are constructed to model superconducting transitions, leading to analytical expressions for the order parameter and London penetration depth. Experimental data from niobium confirm the model's applicability, showing excellent fits and capturing asymmetric scaling behavior around Tc. This work offers a bridge between classical mean-field theory and generalized scaling frameworks, with implications for both theoretical modeling and experimental analysis.

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arXiv2025

Behavior of passive polymeric tracers of different topologies in a dilute bath of active Brownian particles

Using computer simulations in two dimensions we investigate the dynamics and structure of passive polymeric tracer with different topologies immersed in a low-density active particle bath. One of the key observations is that polymer exhibit faster dynamics compared to passive colloidal particles at high activity, for the same particle density, in both linear and star polymer topologies. This enhanced motion is attributed to the accumulation of active particles, which induces prolonged and persistent movement of the polymer. Further analysis reveals that star polymers exhibit more complex and intriguing behavior than their linear counterparts. Notably, the accumulation of active particles promotes the pairing of arms in star polymers. For instance, a three-armed star polymer adopts a conformation similar to a linear polymer with two-arms due to this pairing as a result, at high activity, the dynamics of both the polymers converge. Finally, we explore the dynamics of a linear polymer with the same total number of beads as the star polymer. Interestingly, at high activity -- where arm pairing in the star polymer is significant -- the star polymer demonstrates faster dynamics than the linear polymer, despite having the identical number of beads. These findings contribute to a broader understanding of the interactions between active and passive components of varying topologies in dilute systems and highlight their potential for innovative applications ranging from materials science to biomedicine.

cond-mat.soft↗

arXiv2025

Fractional Brownian motion with mean-density interaction: a myopic self-avoiding fractional stochastic process

Fractional Brownian motion is a Gaussian stochastic process with long-range correlations in time; it has been shown to be a useful model of anomalous diffusion. Here, we investigate the effects of mutual interactions in an ensemble of particles undergoing fractional Brownian motion. Specifically, we introduce a mean-density interaction in which each particle in the ensemble is coupled to the gradient of the total, time-integrated density produced by the entire ensemble. We report the results of extensive computer simulations for the mean-squared displacements and the probability densities of particles undergoing one-dimensional fractional Brownian motion with such a mean-density interaction. We find two qualitatively different regimes, depending on the anomalous diffusion exponent $α$ characterizing the fractional Gaussian noise. The motion is governed by the interactions for $α< 4/3$ whereas it is dominated by the fractional Gaussian noise for $α> 4/3$. We develop a scaling theory explaining our findings. We also discuss generalizations to higher space dimensions and nonlinear interactions, the relation of our process to the ``true'' or myopic self-avoiding walk, as well as applications to the growth of strongly stochastic axons (e.g., serotonergic fibers) in vertebrate brains.

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arXiv2025

Objective comparison of methods to decode anomalous diffusion

Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest, e.g., short or noisy trajectories, heterogeneous behaviour, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. To perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams applied their algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, machine-learning-based approaches achieved superior performance for all tasks. The discussion of the challenge results provides practical advice for users and a benchmark for developers.

physics.data-an↗

arXiv2025

Quantitative evaluation of methods to analyze motion changes in single-particle experiments

The analysis of live-cell single-molecule imaging experiments can reveal valuable information about the heterogeneity of transport processes and interactions between cell components. These characteristics are seen as motion changes in the particle trajectories. Despite the existence of multiple approaches to carry out this type of analysis, no objective assessment of these methods has been performed so far. Here, we report the results of a competition to characterize and rank the performance of these methods when analyzing the dynamic behavior of single molecules. To run this competition, we implemented a software library that simulates realistic data corresponding to widespread diffusion and interaction models, both in the form of trajectories and videos obtained in typical experimental conditions. The competition constitutes the first assessment of these methods, providing insights into the current limitations of the field, fostering the development of new approaches, and guiding researchers to identify optimal tools for analyzing their experiments.

cond-mat.soft↗

arXiv2025

Exactly solvable diffusions from space-time transformations

We consider a general one-dimensional overdamped diffusion model described by the Itô stochastic differential equation (SDE) ${dX_t=μ(X_t,t)dt+σ(X_t,t)dW_t}$, where $W_t$ is the standard Wiener process. We obtain a specific condition that $μ$ and $σ$ must fulfil in order to be able to solve the SDE via mapping the generic process, using a suitable space-time transformation, onto the simpler Wiener process. By taking advantage of this transformation, we obtain the propagator in the case of open, reflecting, and absorbing \emph{time-dependent\/} boundary conditions for a large class of diffusion processes. In particular, this allows us to derive the first-passage time statistics of such a large class of models, some of which were so far unknown. While our results are valid for a wide range of non-autonomous, non-linear and non-homogeneous processes, we illustrate applications in stochastic thermodynamics by focusing on the propagator and first-passage-time statistics of isoentropic processes that were previously realized in the laboratory with Brownian particles trapped with optical tweezers.

cond-mat.stat-mech↗

arXiv2025

Heterogeneous diffusion in an harmonic potential: the role of the interpretation

Diffusion in heterogeneous energy and diffusivity landscapes is widespread in biological systems. However, solving the Langevin equation in such environments introduces ambiguity due to the interpretation parameter $α$, which depends on the underlying physics and can take values in the range $0<α<1$. The typical interpretations are Itô ($α=0$), Stratonovich ($α=1/2$), and Hänggi-Klimontovich ($α=1$). Here, we analyse the motion of a particle in an harmonic potential -- modelled as an Ornstein-Uhlenbeck process -- with diffusivity that varies in space. Our focus is on two-phase systems with a discontinuity in environmental properties at $x=0$. We derive the probability density of the particle position for the process, and consider two paradigmatic situations. In the first one, the damping coefficient remains constant, and fluctuation-dissipation relations are not satisfied. In the second one, these relations are enforced, leading to a position-dependent damping coefficient. In both cases, we provide solutions as a function of the interpretation parameter $α$, with particular attention to the Itô, Stratonovich, and Hänggi-Klimontovich interpretations, revealing fundamentally different behaviours, in particular with respect to an interface located at the potential minimum.

cond-mat.stat-mech↗

arXiv2025

Fastest first-passage time statistics for time-dependent particle injection

A common scenario in a variety of biological systems is that multiple particles are searching in parallel for an immobile target located in a bounded domain, and the fastest among them that arrives to the target first triggers a given desirable or detrimental process. The statistics of such extreme events -- the \textit{fastest\/} first-passage to the target -- is well-understood by now through a series of theoretical analyses, but exclusively under the assumption that all $N$ particles start \textit{simultaneously\/}, i.e., all are introduced into the domain instantly, by $δ$-function-like pulses. However, in many practically important situations this is not the case: in order to start their search, the particles often have to enter first into a bounded domain, e.g., a cell or its nucleus, penetrating through gated channels or nuclear pores. This entrance process has a random duration so that the particles appear in the domain sequentially and with a time delay. Here we focus on the effect of such an extended-in-time injection of multiple particles on the fastest first-passage time (fFPT) and its statistics. We derive the full probability density function $H_N(t)$ of the fFPT with an arbitrary time-dependent injection intensity of $N$ particles. Under rather general assumptions on the survival probability of a single particle and on the injection intensity, we derive the large-$N$ asymptotic formula for the mean fFPT, which is quite different from that obtained for the instantaneous $δ$-pulse injection. The extended injection is also shown to considerably slow down the convergence of $H_N(t)$ to the large-$N$ limit -- the Gumbel distribution -- so that the latter may be inapplicable in the most relevant settings with few tens to few thousands of particles.

cond-mat.stat-mech↗

arXiv2025

Different behaviors of diffusing diffusivity dynamics based on three different definitions of fractional Brownian motion

The effects of a "diffusing diffusivity" (DD), a stochastically time-varying diffusion coefficient, are explored within the frameworks of three different forms of fractional Brownian motion (FBM): (i) the Langevin equation driven by fractional Gaussian noise (LE-FBM), (ii) the Weyl integral representation introduced by Mandelbrot and van Ness (MN-FBM), and (iii) the Riemann-Liouville fractional integral representation (RL-FBM) due to L{é}vy. The statistical properties of the three FBM-generalized DD models are examined, including the mean-squared displacement (MSD), mean-squared increment (MSI), autocovariance function (ACVF) of increments, and the probability density function (PDF). Despite the long-believed equivalence of MN-FBM and LE-FBM, their corresponding FBM-DD models exhibit distinct behavior in terms of the MSD and MSI. In the MN-FBM-DD model, the statistical characteristics directly reflect an effective diffusivity equal to its mean value. In contrast, in LE-FBM-DD, correlations in the random diffusivity give rise to an unexpected crossover behavior in both MSD and MSI. We also find that the MSI and ACVF are nonstationary in RL-FBM-DD but stationary in the other two DD models. All DD models display a crossover from a short-time non-Gaussian PDF to a long-time Gaussian PDF. Our findings offer guidance for experimentalists in selecting appropriate FBM-generalized models to describe viscoelastic yet non-Gaussian dynamics in bio- and soft-matter systems with heterogeneous environments.

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