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Register a new userMass concentration and aging in the parabolic Anderson model with doubly-exponential tails
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We study the solutions $u=u(x,t)$ to the Cauchy problem on $mathbb Z^dtimes(0,infty)$ for the parabolic equation $partial_t u=Delta u+xi u$ with initial data $u(x,0)=1_{{0}}(x)$. Here $Delta$ is the discrete Laplacian on $mathbb Z^d$ and $xi=(xi(z))_{zinmathbb Z^d}$ is an i.i.d. random field with doubly-exponential upper tails. We prove that, for large $t$ and with large probability, a majority of the total mass $U(t):=sum_x u(x,t)$ of the solution resides in a bounded neighborhood of a site $Z_t$ that achieves an optimal compromise between the local Dirichlet eigenvalue of the Anderson Hamiltonian $Delta+xi$ and the distance to the origin. The processes $tmapsto Z_t$ and $t mapsto tfrac1t log U(t)$ are shown to converge in distribution under suitable scaling of space and time. Aging results for $Z_t$, as well as for the solution to the parabolic problem, are also established. The proof uses the characterization of eigenvalue order statistics for $Delta+xi$ in large sets recently proved by the first two authors.
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We consider the parabolic Anderson problem $partial_t u=kappaDelta u+xi u$ on $(0,infty)times Z^d$ with random i.i.d. potential $xi=(xi(z))_{zinZ^d}$ and the initial condition $u(0,cdot)equiv1$. Our main assumption is that $esssupxi(0)=0$. Depending on the thickness of the distribution $prob(xi(0)incdot)$ close to its essential supremum, we identify both the asymptotics of the moments of $u(t,0)$ and the almost-sure asymptotics of $u(t,0)$ as $ttoinfty$ in terms of variational problems. As a by-product, we establish Lifshitz tails for the random Schrodinger operator $-kappaDelta-xi$ at the bottom of its spectrum. In our class of $xi$ distributions, the Lifshitz exponent ranges from $d/2$ to $infty$; the power law is typically accompanied by lower-order corrections.
We present novel martingale concentration inequalities for martingale differences with finite Orlicz-$psi_alpha$ norms. Such martingale differences with weak exponential-type tails scatters in many statistical applications and can be heavier than sub-exponential distributions. In the case of one dimension, we prove in general that for a sequence of scalar-valued supermartingale difference, the tail bound depends solely on the sum of squared Orlicz-$psi_alpha$ norms instead of the maximal Orlicz-$psi_alpha$ norm, generalizing the results of Lesigne & Volny (2001) and Fan et al. (2012). In the multidimensional case, using a dimension reduction lemma proposed by Kallenberg & Sztencel (1991) we show that essentially the same concentration tail bound holds for vector-valued martingale difference sequences.
In [1] a detailed analysis was given of the large-time asymptotics of the total mass of the solution to the parabolic Anderson model on a supercritical Galton-Watson random tree with an i.i.d. random potential whose marginal distribution is double-exponential. Under the assumption that the degree distribution has bounded support, two terms in the asymptotic expansion were identified under the quenched law, i.e., conditional on the realisation of the random tree and the random potential. The second term contains a variational formula indicating that the solution concentrates on a subtree with minimal degree according to a computable profile. The present paper extends the analysis to degree distributions with unbounded support. We identify the weakest condition on the tail of the degree distribution under which the arguments in [1] can be pushed through. To do so we need to control the occurrence of large degrees uniformly in large subtrees of the Galton-Watson tree.
Let ${u(t,, x)}_{t >0, x inmathbb{R}}$ denote the solution to the parabolic Anderson model with initial condition $delta_0$ and driven by space-time white noise on $mathbb{R}_+timesmathbb{R}$, and let $bm{p}_t(x):= (2pi t)^{-1/2}exp{-x^2/(2t)}$ denote the standard Gaussian heat kernel on the line. We use a non-trivial adaptation of the methods in our companion papers cite{CKNP,CKNP_b} in order to prove that the random field $xmapsto u(t,,x)/bm{p}_t(x)$ is ergodic for every $t >0$. And we establish an associated quantitative central limit theorem following the approach based on the Malliavin-Stein method introduced in Huang, Nualart, and Viitasaari cite{HNV2018}.
We consider the large-time behavior of the solution $ucolon [0,infty)timesZto[0,infty)$ to the parabolic Anderson problem $partial_t u=kappaDelta u+xi u$ with initial data $u(0,cdot)=1$ and non-positive finite i.i.d. potentials $(xi(z))_{zinZ}$. Unlike in dimensions $dge2$, the almost-sure decay rate of $u(t,0)$ as $ttoinfty$ is not determined solely by the upper tails of $xi(0)$; too heavy lower tails of $xi(0)$ accelerate the decay. The interpretation is that sites $x$ with large negative $xi(x)$ hamper the mass flow and hence screen off the influence of more favorable regions of the potential. The phenomenon is unique to $d=1$. The result answers an open question from our previous study cite{BK00} of this model in general dimension.
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