arXiv subjects

Satya N. Majumdar

Publications and source records attributed to Satya N. Majumdar.

At least 379 records · Page 21Linked to original sources

arXiv2002

The Local and the Occupation Time of a Particle Diffusing in a Random Medium

We consider a particle moving in a one dimensional potential which has a symmetric deterministic part and a quenched random part. We study analytically the probability distributions of the local time (spent by the particle around its mean value) and the occupation time (spent above its mean value) within an observation time window of size t. The random part of the potential is same as in the Sinai model, i.e., the potential itself is a random walk in space. In the absence of the random potential, these distributions have three typical asymptotic behaviors depending on whether the deterministic potential is unstable, stable or flat. These asymptotic behaviors are shown to get drastically modified when the random part of the potential is switched on leading to the loss of self-averaging and wide sample to sample fluctuations.

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arXiv2001

Persistence of a Continuous Stochastic Process with Discrete-Time Sampling: Non-Markov Processes

We consider the problem of `discrete-time persistence', which deals with the zero-crossings of a continuous stochastic process, X(T), measured at discrete times, T = n(ΔT). For a Gaussian Stationary Process the persistence (no crossing) probability decays as exp(-θ_D T) = [ρ(a)]^n for large n, where a = \exp[-(ΔT)/2], and the discrete persistence exponent, θ_D, is given by θ_D = \ln(ρ)/2\ln(a). Using the `Independent Interval Approximation', we show how θ_D varies with (ΔT) for small (ΔT) and conclude that experimental measurements of persistence for smooth processes, such as diffusion, are less sensitive to the effects of discrete sampling than measurements of a randomly accelerated particle or random walker. We extend the matrix method developed by us previously [Phys. Rev. E 64, 015151(R) (2001)] to determine ρ(a) for a two-dimensional random walk and the one-dimensional random acceleration problem. We also consider `alternating persistence', which corresponds to a < 0, and calculate ρ(a) for this case.

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arXiv2001

Distribution of the Oscillation Period in the Underdamped One Dimensional Sinai Model

We consider the Newtonian dynamics of a massive particle in a one dimemsional random potential which is a Brownian motion in space. This is the zero temperature nondamped Sinai model. As there is no dissipation the particle oscillates between two turning points where its kinetic energy becomes zero. The period of oscillation is a random variable fluctuating from sample to sample of the random potential. We compute the probability distribution of this period exactly and show that it has a power law tail for large period, P(T)\sim T^{-5/3} and an essential singluarity P(T)\sim \exp(-1/T) as T\to 0. Our exact results are confirmed by numerical simulations and also via a simple scaling argument.

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arXiv2001

Statistics of Multiple Sign Changes in a Discrete Non-Markovian Sequence

We study analytically the statistics of multiple sign changes in a discrete non-Markovian sequence ,ψ_i=ϕ_i+ϕ_{i-1} (i=1,2....,n) where ϕ_i's are independent and identically distributed random variables each drawn from a symmetric and continuous distribution ρ(ϕ). We show that the probability P_m(n) of m sign changes upto n steps is universal, i.e., independent of the distribution ρ(ϕ). The mean and variance of the number of sign changes are computed exactly for all n>0. We show that the generating function {\tilde P}(p,n)=\sum_{m=0}^{\infty}P_m(n)p^m\sim \exp[-θ_d(p)n] for large n where the `discrete' partial survival exponent θ_d(p) is given by a nontrivial formula, θ_d(p)=\log[{{\sin}^{-1}(\sqrt{1-p^2})}/{\sqrt{1-p^2}}] for 0\le p\le 1. We also show that in the natural scaling limit when m is large, n is large but but keeping x=m/n fixed, P_m(n)\sim \exp[-n Φ(x)] where the large deviation function Φ(x) is computed. The implications of these results for Ising spin glasses are discussed.

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arXiv2001

Persistence of a Continuous Stochastic Process with Discrete-Time Sampling

We introduce the concept of `discrete-time persistence', which deals with zero-crossings of a continuous stochastic process, X(T), measured at discrete times, T = n ΔT. For a Gaussian Markov process with relaxation rate μ, we show that the persistence (no crossing) probability decays as ρ(a)^n for large n, where a = \exp(-μΔT), and we compute ρ(a) to high precision. We also define the concept of `alternating persistence', which corresponds to a<0. For a>1, corresponding to motion in an unstable potential (μ<0), there is a nonzero probability of having no zero-crossings in infinite time, and we show how to calculate it.

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arXiv2001

Extreme Value Statistics and Traveling Fronts: An Application to Computer Science

We study the statistics of height and balanced height in the binary search tree problem in computer science. The search tree problem is first mapped to a fragmentation problem which is then further mapped to a modified directed polymer problem on a Cayley tree. We employ the techniques of traveling fronts to solve the polymer problem and translate back to derive exact asymptotic properties in the original search tree problem. The second mapping allows us not only to re-derive the already known results for random binary trees but to obtain new exact results for search trees where the entries arrive according to an arbitrary distribution, not necessarily randomly. Besides it allows us to derive the asymptotic shape of the full probability distribution of height and not just its moments. Our results are then generalized to $m$-ary search trees with arbitrary distribution. An attempt has been made to make the article accessible to both physicists and computer scientists.

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arXiv2001

Persistence in a Stationary Time-series

We study the persistence in a class of continuous stochastic processes that are stationary only under integer shifts of time. We show that under certain conditions, the persistence of such a continuous process reduces to the persistence of a corresponding discrete sequence obtained from the measurement of the process only at integer times. We then construct a specific sequence for which the persistence can be computed even though the sequence is non-Markovian. We show that this may be considered as a limiting case of persistence in the diffusion process on a hierarchical lattice.

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arXiv2001

Extremal properties of random trees

We investigate extremal statistical properties such as the maximal and the minimal heights of randomly generated binary trees. By analyzing the master evolution equations we show that the cumulative distribution of extremal heights approaches a traveling wave form. The wave front in the minimal case is governed by the small-extremal-height tail of the distribution, and conversely, the front in the maximal case is governed by the large-extremal-height tail of the distribution. We determine several statistical characteristics of the extremal height distribution analytically. In particular, the expected minimal and maximal heights grow logarithmically with the tree size, N, hmin ~ vmin ln N, and hmax ~ vmax ln N, with vmin=0.373365 and vmax=4.31107, respectively. Corrections to this asymptotic behavior are of order O(ln ln N).

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arXiv2001

Exact Tagged Particle Correlations in the Random Average Process

We study analytically the correlations between the positions of tagged particles in the random average process, an interacting particle system in one dimension. We show that in the steady state the mean squared auto-fluctuation of a tracer particle grows subdiffusively as $sigma^2(t) ~ t^{1/2}$ for large time t in the absence of external bias, but grows diffusively $sigma^2(t) ~ t$ in the presence of a nonzero bias. The prefactors of the subdiffusive and diffusive growths as well as the universal scaling function describing the crossover between them are computed exactly. We also compute $sigma_r^2(t)$, the mean squared fluctuation in the position difference of two tagged particles separated by a fixed tag shift r in the steady state and show that the external bias has a dramatic effect in the time dependence of $sigma_r^2(t)$. For fixed r, $sigma_r^2(t)$ increases monotonically with t in absence of bias but has a non-monotonic dependence on t in presence of bias. Similarities and differences with the simple exclusion process are also discussed.

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arXiv2001

Conserved Mass Models and Particle Systems in One Dimension

In this paper we study analytically a simple one dimensional model of mass transport. We introduce a parameter $p$ that interpolates between continuous time dynamics ($p\to 0$ limit) and discrete parallel update dynamics ($p=1$). For each $p$, we study the model with (i) both continuous and discrete masses and (ii) both symmetric and asymmetric transport of masses. In the asymmetric continuous mass model, the two limits $p=1$ and $p\to 0$ reduce respectively to the $q$-model of force fluctuations in bead packs [S.N. Coppersmith et. al., Phys. Rev. E. {\bf 53}, 4673 (1996)] and the recently studied asymmetric random average process [J. Krug and J. Garcia, cond-mat/9909034]. We calculate the steady state mass distribution function $P(m)$ assuming product measure and show that it has an algebraic tail for small $m$, $P(m)\sim m^{-β}$ where the exponent $β$ depends continuously on $p$. For the asymmetric case we find $β(p)=(1-p)/(2-p)$ for $0\leq p <1$ and $β(1)=-1$ and for the symmetric case, $β(p)=(2-p)^2/(8-5p+p^2)$ for all $0\leq p\leq 1$. We discuss the conditions under which the product measure ansatz is exact. We also calculate exactly the steady state mass-mass correlation function and show that while it decouples in the asymmetric model, in the symmetric case it has a nontrivial spatial oscillation with an amplitude decaying exponentially with distance.

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arXiv2001

Extreme Value Statistics of Hierarchically Correlated Variables: Deviation from Gumbel Statistics and Anomalous Persistence

We study analytically the distribution of the minimum of a set of hierarchically correlated random variables $E_1$, $E_2$, $...$, $E_N$ where $E_i$ represents the energy of the $i$-th path of a directed polymer on a Cayley tree. If the variables were uncorrelated, the minimum energy would have an asymptotic Gumbel distribution. We show that due to the hierarchical correlations, the forward tail of the distribution of the minimum energy becomes highly nnon universal, depends explicitly on the distribution of the bond energies $ε$ and is generically different from the super-exponential forward tail of the Gumbel distribution. The consequence of these results to the persistence of hierarchically correlated random variables is discussed and the persistence is also shown to be generically anomalous.

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arXiv2001

Fluctuation-Dominated Phase Ordering Driven by Stochastically Evolving Surfaces

We study a new kind of phase ordering phenomenon in coarse-grained depth (CD) models of the hill-valley profile of fluctuating surfaces with zero overall tilt, and for hard-core particles sliding on such surfaces under gravity. For Edwards-Wilkinson (EW) and Kardar-Parisi-Zhang (KPZ) surfaces, our analytic and numerical results show that CD models exhibit coarsening to an ordered steady state in which the order parameter has a broad distribution even in the thermodynamic limit. Moreover, the distribution of particle cluster sizes decays as a power-law (with an exponent $θ$), and the scaled 2-point spatial correlation function has a cusp (with an exponent $α= 1/2$) at small values of the argument. The latter feature indicates a deviation from the Porod law. For linear CD models with dynamical exponent $z$, we show that $α= (z - 1)/2$ for $z < 3$, while $α= 1$ for $z > 3$, and there are logarithmic corrections for $z = 3$. This implies $α= 1/2$ for the EW surface and 1 for the Golubovic-Bruinsma-Das Sarma-Tamborenea (GBDT) surface. Within the independent interval approximation we show that $α+ θ= 2$. The scaled density-density correlation function of the sliding particle model shows a cusp with exponent $α\simeq 0.5$, and 0.25 for the EW and KPZ surfaces. The particles on the GBDT surface show conventional coarsening (Porod) behavior with $α\simeq 1$.

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arXiv2000

Spatial Persistence of Fluctuating Interfaces

We show that the probability, P_0(l), that the height of a fluctuating (d+1)-dimensional interface in its steady state stays above its initial value up to a distance l, along any linear cut in the d-dimensional space, decays as P_0(l) \sim l^(-θ). Here θis a `spatial' persistence exponent, and takes different values, θ_s or θ_0, depending on how the point from which l is measured is specified. While θ_s is related to fractional Brownian motion, and can be determined exactly, θ_0 is non-trivial even for Gaussian interfaces.

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arXiv2000

Exact Phase Diagram of a model with Aggregation and Chipping

We revisit a simple lattice model of aggregation in which masses diffuse and coalesce upon contact with rate 1 and every nonzero mass chips off a single unit of mass to a randomly chosen neighbour with rate $w$. The dynamics conserves the average mass density $ρ$ and in the stationary state the system undergoes a nonequilibrium phase transition in the $(ρ-w)$ plane across a critical line $ρ_c(w)$. In this paper, we show analytically that in arbitrary spatial dimensions, $ρ_c(w) = \sqrt{w+1}-1$ exactly and hence, remarkably, independent of dimension. We also provide direct and indirect numerical evidence that strongly suggest that the mean field asymptotic answer for the single site mass distribution function and the associated critical exponents are super-universal, i.e., independent of dimension.

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arXiv2000

Extremal Paths on a Random Cayley Tree

We investigate the statistics of extremal path(s) (both the shortest and the longest) from the root to the bottom of a Cayley tree. The lengths of the edges are assumed to be independent identically distributed random variables drawn from a distribution ρ(l). Besides, the number of branches from any node is also random. Exact results are derived for arbitrary distribution ρ(l). In particular, for the binary {0,1} distribution ρ(l)=pδ_{l,1}+(1-p)δ_{l,0}, we show that as p increases, the minimal length undergoes an unbinding transition from a `localized' phase to a `moving' phase at the critical value, p=p_c=1-b^{-1}, where b is the average branch number of the tree. As the height n of the tree increases, the minimal length saturates to a finite constant in the localized phase (p p_c) where the velocity v_{min}(p) is determined via a front selection mechanism. At p=p_c, the minimal length grows with n in an extremely slow double logarithmic fashion. The length of the maximal path, on the other hand, increases linearly as v_{max}(p)n for all p. The maximal and minimal velocities satisfy a general duality relation, v_{min}(p)+v_{max}(1-p)=1, which is also valid for directed paths on finite-dimensional lattices.

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arXiv2000

Traveling Waves, Front Selection, and Exact Nontrivial Exponents in a Random Fragmentation Problem

We study a random bisection problem where an initial interval of length x is cut into two random fragments at the first stage, then each of these two fragments is cut further, etc. We compute the probability P_n(x) that at the n-th stage, each of the 2^n fragments is shorter than 1. We show that P_n(x) approaches a traveling wave form, and the front position x_n increases as x_n\sim n^βρ^n for large n. We compute exactly the exponents ρ=1.261076... and β=0.453025.... as roots of transcendental equations. We also solve the m-section problem where each interval is broken into m fragments. In particular, the generalized exponents grow as ρ_m\approx m/(\ln m) and β_m\approx 3/(2\ln m) in the large m limit. Our approach establishes an intriguing connection between extreme value statistics and traveling wave propagation in the context of the fragmentation problem.

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arXiv2000

Slow Logarithmic Decay of Magnetization in the Zero Temperature Dynamics of an Ising Spin Chain: Analogy to Granular Compaction

We study the zero temperature coarsening dynamics in an Ising chain in presence of a dynamically induced field that favors locally the `-' phase compared to the `+' phase. At late times, while the `+' domains still coarsen as $t^{1/2}$, the `-' domains coarsen slightly faster as $t^{1/2}\log (t)$. As a result, at late times, the magnetization decays slowly as, $m(t)=-1 +{\rm const.}/{\log (t)}$. We establish this behavior both analytically within an independent interval approximation (IIA) and numerically. In the zero volume fraction limit of the `+' phase, we argue that the IIA becomes asymptotically exact. Our model can be alternately viewed as a simple Ising model for granular compaction. At late times in our model, the system decays into a fully compact state (where all spins are `-') in a slow logarithmic manner $\sim 1/{\log (t)}$, a fact that has been observed in recent experiments on granular systems.

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arXiv2000

Exact Calculation of the Spatio-temporal Correlations in the Takayasu model and in the q-model of Force Fluctuations in Bead Packs

We calculate exactly the two point mass-mass correlations in arbitrary spatial dimensions in the aggregation model of Takayasu. In this model, masses diffuse on a lattice, coalesce upon contact and adsorb unit mass from outside at a constant rate. Our exact calculation of the variance of mass at a given site proves explicitly, without making any assumption of scaling, that the upper critical dimension of the model is 2. We also extend our method to calculate the spatio-temporal correlations in a generalized class of models with aggregation, fragmentation and injection which include, in particular, the $q$-model of force fluctuations in bead packs. We present explicit expressions for the spatio-temporal force-force correlation function in the $q$-model. These can be used to test the applicability of the $q$-model in experiments.

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