arXiv subjects

Alexander K. Hartmann

Publications and source records attributed to Alexander K. Hartmann.

At least 19 recordsLinked to original sources

arXiv2026

Splitting probabilities for Brownian motion with diffusing boundaries: Application to polymer translocation

We study the translocation of a polymer chain through a nanopore where the chain length fluctuates stochastically due to the polymerization-depolymerization processes at the chain ends. We map this process to an equivalent representation where the pore performs a stochastic random-walk-like process on a line in the presence of two diffusing sinks on either side of it with diffusion constants $D_1$ and $D_3$ respectively. The translocation process terminates when the pore hits either of the two outer diffusing sinks. In the case where the pore motion itself is diffusive with diffusion constant $D_2$, we compute exactly the splitting probability that the pore hits the left (right) sink before hitting the right (left) sink. We show that the splitting probability in the presence of mobile sinks is rather nontrivial compared to the classical case of immobile sinks (the latter corresponds to the case when the chain length is fixed). Furthermore, we also compute exactly the probability distribution of the translocation time and that of the chain length at the completion time of the translocation. We show that both distributions have power law tails with exponents that depend continuously on the diffusion constants $D_1$, $D_2$ and $D_3$. We validate our analytical predictions via numerical simulations. We then present numerical results for the case when the pore performs a fractional Brownian motion with Hurst exponent $0<H<1$, while the sinks are still diffusive.

cond-mat.stat-mech↗

arXiv2026

Random walks in Dirichlet random environment in dimension $d+1$

The atypical behaviour of random walks in time-dependent random environment was recently related to Kardar-Parisi-Zhang (KPZ) growth. While this is now well-understood in spatial dimension $d=1$, further efforts are necessary to better understand these connections in dimensions $d>1$. In this paper, we study this problem numerically for $d=1, 2$ and $3$, focusing on a discrete model with Dirichlet distributed transition probabilities. This model is a generalization of an integrable model in $d=1$, and it has the advantage of admitting an explicit, product-form, stationary measure. We verify that the growth of the variance of the logarithm of point-to-point probabilities, namely from the origin to position $x$ in time $t$, is compatible with KPZ growth in dimension $d=1$ and $d=2$. In spatial dimension $d=3$, we confirm the existence of a phase transition as the angle $\vert x\vert /t$ increases and we obtain a lower bound based on an exact second moment calculation. We find that in the weak disorder phase the point-to-point probability acquires a heavy tailed distribution, and that in the strong disorder phase the cumulants of its logarithm grow with time. Further, we show that for this model, we can compute exactly the sample to sample variance of the thermal average $\overline{ \langle x \rangle^2}$ and that it is related to the extreme diffusion coefficient introduced recently.

cond-mat.stat-mech↗

arXiv2026

Level statistics in the fractal phase of generalized Rosenzweig--Porter models

The Rosenzweig--Porter (RP) random matrix ensemble has emerged as a minimal model for the integrability-to-chaos crossover in quantum many-body systems. Its phase diagram features a region with fractal eigenstates, exhibiting intermediate spectral and localization properties between the fully localized and fully delocalized regimes. In this work, we explore several generalizations of the RP model and determine their level statistics at the scale of the Thouless energy $E_T$, which characterizes the crossover. Using tools from free probability theory and the replica method, we compute the full counting statistics in the limit of large system size, and show that it takes a simple, universal scaling form around $E_T$, shared across all variations of the model. We validate our analytical predictions using exact numerical diagonalization of large samples, and large-deviation algorithms that resolve the full counting statistics down to probabilities as low as $10^{-40}$. We also contrast our predictions with measurements on the quantum random energy model, which is the simplest model displaying many-body localization.

cond-mat.dis-nn↗

arXiv2025

Large Deviation Properties of Minimum Spanning Trees for Random Graphs

We study the large-deviation properties of minimum spanning trees for two ensembles of random graphs with $N$ nodes. First, we consider complete graphs. Second, we study Erd\H{o}s-R\'{e}nyi (ER) random graphs with edge probability $p=c/N$ conditioned to be connected. By using large-deviation Markov chain sampling, we are able to obtain the distribution $P(W)$ of the spanning-tree weight $W$ down to probability densities as small as $10^{-300}$. For the complete graph, we confirm analytical predictions with respect to the expectation value. For both ensembles, the large deviation principle is fulfilled. For the connected ER graphs, we observe a remarkable change of the distributions at the value of $c=1$, which is the percolation threshold for the original ER ensemble.

cond-mat.dis-nn↗

arXiv2025

Diffusion with stochastic resetting on a lattice

We provide an exact formula for the mean first-passage time (MFPT) to a target at the origin for a single particle diffusing on a $d$-dimensional hypercubic {\em lattice} starting from a fixed initial position $\vec R_0$ and resetting to $\vec R_0$ with a rate $r$. Previously known results in the continuous space are recovered in the scaling limit $r\to 0$, $R_0=|\vec R_0|\to \infty$ with the product $\sqrt{r}\, R_0$ fixed. However, our formula is valid for any $r$ and any $\vec R_0$ that enables us to explore a much wider region of the parameter space that is inaccessible in the continuum limit. For example, we have shown that the MFPT, as a function of $r$ for fixed $\vec R_0$, diverges in the two opposite limits $r\to 0$ and $r\to \infty$ with a unique minimum in between, provided the starting point is not a nearest neighbour of the target. In this case, the MFPT diverges as a power law $\sim r^{\phi}$ as $r\to \infty$, but very interestingly with an exponent $\phi= (|m_1|+|m_2|+\ldots +|m_d|)-1$ that depends on the starting point $\vec R_0= a\, (m_1,m_2,\ldots, m_d)$ where $a$ is the lattice spacing and $m_i$'s are integers. If, on the other hand, the starting point happens to be a nearest neighbour of the target, then the MFPT decreases monotonically with increasing $r$, approaching a universal limiting value $1$ as $r\to \infty$, indicating that the optimal resetting rate in this case is infinity. We provide a simple physical reason and a simple Markov-chain explanation behind this somewhat unexpected universal result. Our analytical predictions are verified in numerical simulations on lattices up to $50$ dimensions. Finally, in the absence of a target, we also compute exactly the position distribution of the walker in the nonequlibrium stationary state that also displays interesting lattice effects not captured by the continuum theory.

cond-mat.stat-mech↗

arXiv2024

Exact joint distributions of three global characteristic times for Brownian motion

We consider three global characteristic times for a one-dimensional Brownian motion $x(\tau)$ in the interval $\tau\in [0,t]$: the occupation time $t_{\rm o}$ denoting the cumulative time where $x(\tau)>0$, the time $t_{\rm m}$ at which the process achieves its global maximum in $[0,t]$ and the last-passage time $t_l$ through the origin before $t$. All three random variables have the same marginal distribution given by L\'evy's arcsine law. We compute exactly the pairwise joint distributions of these three times and show that they are quite different from each other. The joint distributions display rather rich and nontrivial correlations between these times. Our analytical results are verified by numerical simulations.

cond-mat.stat-mech↗

arXiv2024

Numerical Estimation of Limiting Large-Deviation Rate Functions

For statistics of rare events in systems obeying a large-deviation principle, the rate function is a key quantity. When numerically estimating the rate function one is always restricted to finite system sizes. Thus, if the interest is in the limiting rate function for infinite system sizes, first, several system sizes have to be studied numerically. Here, rare-event algorithms using biased ensembles give access to the low-probability region. Second, some kind of system-size extrapolation has to be performed. Here we demonstrate how rare-event importance sampling schemes can be combined with multi-histogram reweighting, which allows for rather general applicability of the approach, independent of specific sampling algorithms. We study two ways of performing the system-size extrapolation, either directly acting on the empirical rate functions, or on the scaled cumulant generating functions, to obtain the infinite-size limit. The presented method is demonstrated for a binomial distributed variable and the largest connected component in Erd\"os-R\'enyi random graphs. Analytical solutions are available in both cases for direct comparison. It is observed in particular that phase transitions appearing in the biased ensembles can lead to systematic deviations from the true result.

physics.data-an↗

arXiv2024

Numerical Aspects of Large Deviations

An introduction to numerical large-deviation sampling is provided. First, direct biasing with a known distribution is explained. As simple example, the Bernoulli experiment is used throughout the text. Next, Markov chain Monte Carlo (MCMC) simulations are introduced. In particular, the Metropolis-Hastings algorithm is explained. As first implementation of MCMC, sampling of the plain Bernoulli model is shown. Next, an exponential bias is used for the same model, which allows one to obtain the tails of the distribution of a measurable quantity. This approach is generalized to MCMC simulations, where the states are vectors of $U(0,1)$ random entries. This allows one to use the exponential or any other bias to access the large-deviation properties of rather arbitrary random processes. Finally, some recent research applications to study more complex models are discussed.

physics.comp-ph↗

arXiv2024

Non-universality for Crossword Puzzle Percolation

A percolation model inspired by crossword puzzle games is introduced. A game proceeds by solving words, which are segments of sites in a two-dimensional lattice. As test case, the \emph{iid} variant allows for independently occupying sites with letters, only the percolation criterion depends on the existence of solved words. For the \emph{game} variant, inspired by real crossword puzzles, it becomes more likely to solve crossing words which share sites with the already solved words. In this way avalanches of solved words may occur. Both model variants exhibit a percolation transition as function of the a-priori site or word solving probability, respectively. The \emph{iid} variant is in the universality class of standard two-dimensional percolation. The \emph{game} variant exhibits a non-universal critical exponent $\nu$ of the correlation length. The actual value of $\nu$ depends on the function which controls how much solved words accelerate the solved of crossing words.

cond-mat.stat-mech↗

arXiv2024

Resetting by rescaling: exact results for a diffusing particle in one-dimension

In this paper, we study a simple model of a diffusive particle on a line, undergoing a stochastic resetting with rate $r$, via rescaling its current position by a factor $a$, which can be either positive or negative. For $|a|<1$, the position distribution becomes stationary at long times and we compute this limiting distribution exactly for all $|a|<1$. This symmetric distribution has a Gaussian shape near its peak at $x=0$, but decays exponentially for large $|x|$. We also studied the mean first-passage time (MFPT) $T(0)$ to a target located at a distance $L$ from the initial position (the origin) of the particle. As a function of the initial position $x$, the MFPT $T(x)$ satisfies a nonlocal second order differential equation and we have solved it explicitly for $0 \leq a < 1$. For $-1<a\leq 0$, we also solved it analytically but up to a constant factor $\kappa$ whose value can be determined independently from numerical simulations. Our results show that, for all $-1<a<1$, the MFPT $T(0)$ (starting from the origin) shows a minimum at $r=r^*(a)$. However, the optimised MFPT $T_{\rm opt}(a)$ turns out to be a monotonically increasing function of $a$ for $-1<a<1$. This demonstrates that, compared to the standard resetting to the origin ($a=0$), while the positive rescaling is not beneficial for the search of a target, the negative rescaling is. Thus resetting via rescaling followed by a reflection around the origin expedites the search of a target in one dimension.

cond-mat.stat-mech↗

arXiv2024

The Griffiths phase and beyond: A large deviations study of the magnetic susceptibility of the two-dimensional bond-diluted Ising model

The Griffiths phase in systems with quenched disorder occurs below the ordering transition of the pure system down to the ordering transition of the actual disordered system. While it does not exhibit long-range order, large fluctuations in the disorder degrees of freedom result in exponentially rare, long-range ordered states and hence the occurrence of broad distributions in response functions. Inside the Griffiths phase of the two-dimensional bond-diluted Ising model the distribution of the magnetic susceptibility is expected to have such a broad, exponential tail. A large-deviations Monte Carlo algorithm is used to sample this distribution and the exponential tail is extracted over a wide range of the support down to very small probabilities of the order of $10^{-300}$. We study the behavior of the susceptibility distribution across the full phase diagram, from the paramagnetic state through the Griffiths phase to the ferromagnetically ordered system and down to the zero-temperature point. We extract the rate function of large-deviation theory as well as its finite-size scaling behavior and we reveal interesting differences and similarities between the cases. A connection between the fraction of ferromagnetic bonds in a given disorder sample and the size of the magnetic susceptibility is demonstrated numerically.

cond-mat.dis-nn↗

arXiv2024

Coexistence of asynchronous and clustered dynamics in noisy inhibitory neural networks

A regime of coexistence of asynchronous and clustered dynamics is analyzed for globally coupled homogeneous and heterogeneous inhibitory networks of quadratic integrate-and-fire (QIF) neurons subject to Gaussian noise. The analysis is based on accurate extensive simulations and complemented by a mean-field description in terms of low-dimensional next generation neural mass models for heterogeneously distributed synaptic couplings. The asynchronous regime is observable at low noise and becomes unstable via a sub-critical Hopf bifurcation at sufficiently large noise. This gives rise to a coexistence region between the asynchronous and the clustered regime. The clustered phase is characterized by population bursts in the {\gamma}-range (30-120 Hz), where neurons are split in two equally populated clusters firing in alternation. This clustering behaviour is quite peculiar: despite the global activity being essentially periodic, single neurons display switching between the two clusters due to heterogeneity and/or noise.

cond-mat.dis-nn↗

arXiv2023

Large-deviation analysis of rare resonances for the Many-Body localization transition

A central theoretical issue at the core of the current research on many-body localization (MBL) consists in characterizing the statistics of rare long-range resonances in many-body eigenstates. This is of paramount importance to understand: (i) the critical properties of the MBL transition and the mechanism for its destabilization through quantum avalanches; (ii) the unusual transport and anomalously slow out-of-equilibrium relaxation when the transition is approached from the metallic side. In order to study and characterize such long-range rare resonances, we develop a large-deviations approach based on an analogy with the physics of directed polymers in random media, and in particular with their freezing glass transition on infinite-dimensional graphs. The basic idea is to enlarge the parameter space by adding an auxiliary parameter (which plays the role of the inverse temperature in the directed polymer formulation) which allows us to fine-tune the effect of anomalously large outliers in the far-tails of the probability distributions of the transmission amplitudes between far-away many-body configurations in the Hilbert space. We first benchmark our approach onto two non-interacting paradigmatic toy models, namely the single-particle Anderson model on the (loop-less) Cayley tree and the Rosenzweig-Porter random matrix ensemble, and then apply it to the study of a class of disordered quantum spin chains in a transverse field. This analysis shows the existence of a broad disorder range in which rare, long-distance resonances, that may form only for a few specific realizations of the disorder and a few specific choice of the random initial state, destabilize the MBL phase, while the genuine MBL transition is shifted to much larger values of the disorder than originally thought.

cond-mat.dis-nn↗

arXiv2023

Optimized Finite-Time Work Protocols for the Higgs RNA-Model

The Higgs RNA-Model is studied in regard to finite-time driving protocols with minimal-work requirement. In this paper, RNA sequences which at low temperature exhibits hairpins are considered, which are often cited as typical template systems in stochastic thermodynamics. The optimized work protocols for this glassy many-particle system are determined numerically using the parallel tempering method. The protocols show distinct jumps at the beginning and end, which have been observed previously already for single-particle systems. Counter intuitively, optimality seems to be achieved by staying close to the equilibrium unfolding transition point. The change of work distributions, compared to those resulting from a naive linear driving protocol, are discussed generally and in terms of free energy estimation as well as the effect of optimized protocols on rare work process starting conditions.

cond-mat.stat-mech↗

arXiv2023

The distribution of the maximum of independent resetting Brownian motions

The probability distribution of the maximum $M_t$ of a single resetting Brownian motion (RBM) of duration $t$ and resetting rate $r$, properly centred and scaled, is known to converge to the standard Gumbel distribution of the classical extreme value theory. This Gumbel law describes the typical fluctuations of $M_t$ around its average $\sim \ln (r t)$ for large $t$ on a scale of $O(1)$. Here we compute the large-deviation tails of this distribution when $M_t = O(t)$ and show that the large-deviation function has a singularity where the second derivative is discontinuous, signalling a dynamical phase transition. Then we consider a collection of independent RBMs with initial (and resetting) positions uniformly distributed with a density $\rho$ over the negative half-line. We show that the fluctuations in the initial positions of the particles modify the distribution of $M_t$. The average over the initial conditions can be performed in two different ways, in analogy with disordered systems: (i) the annealed case where one averages over all possible initial conditions and (ii) the quenched case where one considers only the contributions coming from typical initial configurations. We show that in the annealed case, the limiting distribution of the maximum is characterized by a new scaling function, different from the Gumbel law but the large-deviation function remains the same as in the single particle case. In contrast, for the quenched case, the limiting (typical) distribution remains Gumbel but the large-deviation behaviors are new and nontrivial. Our analytical results, both for the typical as well as for the large-deviation regime of $M_t$, are verified numerically with extremely high precision, down to $10^{-250}$ for the probability density of $M_t$.

cond-mat.stat-mech↗

arXiv2023

Probing the large deviations for the Beta random walk in random medium

We consider a discrete-time random walk on a one-dimensional lattice with space and time-dependent random jump probabilities, known as the Beta random walk. We are interested in the probability that, for a given realization of the jump probabilities (a sample), a walker starting at the origin at time $t=0$ is at position beyond $ξ\sqrt{T/2}$ at time $T$. This probability fluctuates from sample to sample and we study the large-deviation rate function which characterizes the tails of its distribution at large time $T \gg 1$. It is argued that, up to a simple rescaling, this rate function is identical to the one recently obtained exactly by two of the authors for the continuum version of the model. That continuum model also appears in the macroscopic fluctuation theory of a class of lattice gases, e.g. in the so-called KMP model of heat transfer. An extensive numerical simulation of the Beta random walk, based on an importance sampling algorithm, is found in good agreement with the detailed analytical predictions. A first-order transition in the tilted measure, predicted to occur in the continuum model, is also observed in the numerics.

cond-mat.stat-mech↗

arXiv2023

Non-Analytic Behaviour in Large-deviations of the SIR model under the influence of Lockdowns

We numerically investigate the dynamics of an SIR model with infection level-based lockdowns on Small-World networks. Using a large-deviation approach, namely the Wang-Landau algorithm, we study the distribution of the cumulative fraction of infected individuals. We are able to resolve the density of states for values as low as $10^{-85}$. Hence, we measure the distribution on its full support giving a complete characterization of this quantity. The lockdowns are implemented by severing a certain fraction of the edges in the Small-World network, and are initiated and released at different levels of infection, which are varied within this study. We observe points of non-analytical behaviour for the pdf and discontinuous transitions for correlations with other quantities such as the maximum fraction of infected and the duration of outbreaks. Further, empirical rate functions were calculated for different system sizes, for which a convergence is clearly visible indicating that the large-deviation principle is valid for the system with lockdowns.

physics.soc-ph↗

arXiv2023

Rare Events of Host Switching for Diseases using a SIR Model with Mutations

We numerically study disease dynamics that lead to the disease switching from one host species to another, resulting in diseases gaining the ability to infect, e.g., humans. Unlike previous studies that focused on branching processes starting with the first infected humans, we begin by considering a disease pathogen that initially cannot infect humans. We model the entire process, starting from an infection in the animal population, including mutations that eventually enable the disease to cause an epidemic outbreak in the human population. We use an SIR model on a network consisting of 132 dog and 1320 human nodes, with a single parameter representing the gene of the pathogen. We use numerical large-deviation techniques, specifically the $1/t$ Wang-Landau algorithm, to calculate the potentially very small probability of the host switching event. With this approach we are able to resolve probabilities as small as $10^{-120}$. Additionally the $1/t$ Wang-Landau algorithm allows us to obtain the complete probability density function $P(C)$ of the cumulative fraction $C$ of infected humans, which is an indicator for the severity of the disease in the human population. We also calculate correlations of $C$ with selected quantities $q$ that characterize the outbreak. Due to the application of the rare-event algorithm, this is possible for the entire range of $C$ values.

physics.soc-ph↗