Global properties of Stochastic Loewner evolution driven by Levy processes

0.88 MB·English

by  P. Oikonomou

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0 Global properties of Stochastic Loewner evolution
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n driven by L´evy processes
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4 P. Oikonomou, I. Rushkin, I. A. Gruzberg, L. P. Kadanoff
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] The James Franck Institute, The University of Chicago
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5640 S. Ellis Avenue, Chicago, Il 60637 USA
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January 14, 2008
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a
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s Abstract
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t
a Standard Schramm-Loewner evolution (SLE) is driven by a con-
m
tinuous Brownian motion which then produces a trace, a continuous
- fractal curve connecting the singular points of the motion. If jumps
d
n areaddedtothedrivingfunction,the tracebranches. Inarecentpub-
o lication[1]we introduceda generalizedSLE driven by a superposition
c of a Brownian motion and a fractal set of jumps (technically a stable
[
L´evy process). We then discussed the small-scale properties of the re-

2 sulting L´evy-SLE growth process. Here we discuss the same model,
v but focus on the global scaling behavior which ensues as time goes to
0
infinity. This limiting behavior is independent of the Brownian forc-
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ing and depends upon only a single parameter, α, which defines the
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2 shape of the stable L´evy distribution. We learn about this behavior
. by studying a Fokker-Planckequation which gives the probability dis-
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tribution for endpoints of the trace as a function of time. As in the
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7 short-time case previously studied, we observe that the properties of
0 this growth process change qualitatively and singularly at α = 1. We
: show both analytically and numerically that the growth continues in-
v
i definitelyintheverticaldirectionforα>1,goesaslogtforα=1,and
X
saturates for α < 1. The probability density has two different scales
r corresponding to directions along and perpendicular to the boundary.
a
In the former case, the characteristic scale is X(t) t1/α. In the lat-
ter case the scale is Y(t) A+Bt1−1/α for α = ∼1, and Y(t) lnt
∼ 6 ∼
for α = 1. Scaling functions for the probability density are given for
various limiting cases.
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Contents
1 Introduction 3
2 The model and the results, old and new 4
3 Derivation of the Fokker-Planck equation 8
4 Qualitative description, distance scales 12
5 Solving the FPE 16
5.1 Results for α > 1 . . . . . . . . . . . . . . . . . . . . . . . . . 19
5.2 Results for α = 1 . . . . . . . . . . . . . . . . . . . . . . . . . 24
5.3 Results for α < 1 . . . . . . . . . . . . . . . . . . . . . . . . . 26
6 Conclusions 30
7 Acknowledgements 31
A Appendices 31
A.1 Numerical calculations . . . . . . . . . . . . . . . . . . . . . . 31
A.2 Asymptotics for K(λ,y) . . . . . . . . . . . . . . . . . . . . . 32
2
1 Introduction
The study of random conformally-invariant clusters that appear at critical
points in two-dimensional statistical mechanics models has been made rig-
orous with theinvention of theso-called Schramm-Loewnerevolution (SLE)
[2]. SLE refers to acontinuous family of evolving conformal maps that spec-
ify the shape of a part of a critical cluster boundary. By now SLE has
been justly recognized as a major breakthrough, and there are several re-
view papers and one monograph devoted to this beautiful subject, see Refs.
[3, 4, 5, 6, 7, 8, 9, 10]
SLE describes a curve, called trace, growing with time from a boundary
in a two-dimensional domain which is usually chosen to be the upper half
plane. SLE is based on the Loewner equation in which the shape of the
growing curve is determined by a function of time ξ(t) which in SLE is
taken to be a scaled Brownian motion. Such a choice of the drivingfunction
produces continuous stochastic, fractal and conformally invariant curves,
— the kind that appears as the scaling limit of various interfaces in many
two-dimensional critical lattice models and growth processes of statistical
physics. Well-known examples include boundaries of the Fortuin-Kastelyn
clusters in the critical q-state Potts model, loops in the O(n) model, self-
avoiding and loop-erased random walks.
In Ref. [1] we generalized SLE to a broader class for which ξ(t) is a
Markov process with discontinuities. More specifically, we have studied the
Loewner evolution driven by a linear combination of a scaled Brownian mo-
tion and a symmetric stable L´evy process. The growing curve then exhibits
branching. This generalized process might be useful to describe many tree-
like growth processes, such as branching polymers and various branching
growth processes which evolve in time.
Such generalized SLEs driven by L´evy processes (L´evy-SLE for short)
have also been of interest to mathematics community. Our results [1] on
variousphasetransitionsinL´evy-SLEhavebeenputonrigorousbasisinRef.
[11], and further properties have been studied in Refs. [12, 13]. The interest
ofmathematicians intheseL´evy-SLE processesispartially motivated bythe
suggestion [13, 14] that they may produce fractal objects with large values
of multifractal exponents for harmonic measure. Harmonic measure can
be thought of as the charge distribution on the boundary of a conducting
cluster. On fractal boundaries such a distribution is a multifractal, and
in the case of critical clusters (whose boundaries are SLE curves) the full
spectrum of multifractal exponents has been obtained analytically, see Refs.
[14, 15, 16, 17, 18] for various derivations and discussion.
3
While our previous paper [1] focused on local properties of L´evy-SLE,
herewestudytheglobal behaviorof thegrowth in theupperhalf plain. The
present paper is structured as follows. In Section 2 we define our model and
briefly state our previous results on phase transitions in the local behavior
of the model. We also present our new results on the global behavior of
L´evy-SLE. In Section 3 we derive the Fokker-Planck equation governing the
evolution of the probability distribution for the tip of the L´evy-SLE. The
equation is our main tool for analysis of the long time global behavior of
the growth. We give a qualitative description of the growth and explain the
approximations that go into the solution of the Fokker-Planck equation in
Section 4. Actual solution of the Fokker-Planck equation and comparison
with results from numerically calculated trajectories is given in Section 5.
We conclude in Section 6. Some technical details are presented in Appen-
dices.
2 The model and the results, old and new
Loewnerevolutionisafamilyofconformalmapsthatappearsasthesolution
of the Loewner differential equation (see, for example, Ref. [3] for details)
2
∂ g (z) = , g (z) = z. (1)
t t 0
g (z) ξ(t)
t
−
valid at any pointz in theupperhalf planeuntil (and if) this point becomes
singular at some (possibly infinite) time τ : ξ(τ ) = g (z). The set of all
z z τz
singularities is called the hull and the point at which the hull grows is called
the tip. The tip γ(t) is defined via its image ξ(t) = g (γ(t)). More formally,
t
γ(t) = lim g−1(w), (2)
t
w→ξ(t)
where the limit is taken in the upper half plane. The trace is the path left
behind by the tip (the existence of the trace in the setting of this paper
has been shown in Ref. [12]). The shape of the growing trace (and the
hull) is completely determined by the driving function ξ(t). At any time
the function g (z) conformally maps the exterior of the growing hull to the
t
upper half plane, see Fig. 1. We refer to the z plane where the growth
occurs as “the physical plane”, and to the w plane as “the mathematical
plane”.
Naturally, if ξ(t) is a stochastic process the shape of the growing trace is
alsostochastic. Thegrowthprocessisthenastochastic(Schramm-)Loewner
4
Figure 1: The Loewner evolution shown for the case when the growing hull
is a smooth curve. The complement of the segment of the curve (up to its
tip γ(t)) in the “physical” z plane is mapped to the entire upper half of the
“mathematical” w plane by the function g (z).
t
evolution (SLE). The standard SLE has a driving function ξ(t) = √κB(t),
where B(t) is a normalized Brownian motion and κ > 0 is the diffusion
constant. Many important properties of this process have been established
in Ref. [19].
In Ref. [1] we have generalized SLE to
ξ(t) = √κB(t)+c1/αL (t), (3)
α
whereL (t)isanormalizedsymmetricα-stableL´evyprocess[20,21,22,23],
α
and c > 0 is the “diffusion constant” associated with it. The process L (t)
α
is composed of a succession of jumps of all sizes. Unlike a Brownian motion,
L (t) is discontinuous on all time-scales. Therefore, the addition of a L´evy
α
processes to the driving force of SLE introduces branching to the trace.
Theprobability distributionfunctionofc1/αL (t)isgiven bytheFourier
α
transform
∞ dk
P(x,t) = e−ikxe−ct|k|α. (4)
2π
Z−∞
As it is known in the theory of stable distributions [23], only for 0 < α 6 2
this Fourier transformgives anon-negative probability density. For 0 < α<
2 the function P(x,t) decays at large distances as a power law:
ct
x : P(x,t) , (5)
→ ∞ ∼ x1+α
| |
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so that the process scales as
L (t)δ tδ/α (6)
α
h| | i ∝
for any δ < α. For δ > α this average is infinite. For α = 2 the process
L (t) is the standard Brownian motion B(t) and P(x,t) is Gaussian.
2
Westudiedtheshort-distancepropertiesoftheL´evy-SLE processinRef.
[1]. At short times and distances the process is dominated by the Brownian
motion and the deterministic drift term (see Eq. (13)), whereas at long
times it is dominated by L´evy flights. The crossover between short and long
time behavior happens at the time
1 1/(2−α)
t . (7)
0 ∼ c2
(cid:16) (cid:17)
This also defines a spatial crossover at length scales l √t . For scales
0 0
∝
smaller than l the trace behaves like standard SLE, while for scales much
0
larger than l it spreads in the x direction forming tree-like structures.
0
In our previous paper [1], using both analytic and numerical considera-
tions, we determined the probability that a point on the x axis is swallowed
by the trace. The trace shows a qualitative change in its small-distance,
small-time behavior as κ and α each pass though critical values, respec-
tively at four and one. The transition at κ = 4 is quite analogous to the
known transition of standard SLE [19]. For the new transition at α = 1, the
trace forms isolated trees when α< 1 or a dense forest when α > 1.
The latter phase transition at α = 1 was recently studied rigorously in
Ref. [11] which expanded the implications of the phase transition to the
whole plane at the limit t . For κ > 4 a point in the upper half plane
→ ∞
is swallowed almost surely for α > 1, while it is swallowed with probability
smaller than one for α < 1. For κ < 4 and 0 < α < 2 the swallowed points
on the plane form a set of measure zero.
Thelarge-scale implications oftheα = 1transitioncan beseeninFigure
2 which shows the shapeof the trace at long times. For α < 1 the stochastic
evolution produces isolated tree-like structures which are limited in height.
For α > 1 the evolution produces an “underbrush” in which structures pile
on one another and thereby continue to increase their height.
In the rest of the paper we establish the following. The growth at long
timesischaracterizedbytwoverydifferentlengthscalesX(t)andY(t)(with
X(t) Y(t)) which can bethought of as thetypical size of the growing hull
≫
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Figure 2: Examples of traces produced by L´evy-SLE at long times, up to
t = 9000. For thefirst, second and last thirdsof thetime interval, thetraces
are correspondingly colored red, blue, and yellow. Top panel: α = 0.7. The
trace looks like many isolated trees whose height saturates at long times.
Bottom panel: α = 1.5. Now the tip of the growing trace keeps landing on
the previously grown “bushes” so that the trace extends indefinitely in the
vertical direction as time increases. Notice the difference in scales for the
y axis between the two panels, as well as the much larger spread in the x
direction inthetoppanel. ThetracewasproducedusingstableL´evy forcing
and c = 10, time step τ = 10−3. The trace was calculated only at times
when the forcing makes a large jump dξ > √200τ.
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in the x and y directions. More specifically, we find that
X(t) t1/α, 0< α < 2, (8)
∼
A+Bt1−1/α, α= 1,
Y(t) 6 (9)
∼ (lnt, α= 1.
(TheconstantsAandB dependuponα.) Thesescalesenterthescalingform
ofthejointprobabilitydistributionρ(x,y,t)fortherealandimaginaryparts
of the tip γ(t) of the L´evy-SLE, for which we give explicit results in various
limiting cases in Section 5, where we also compare analytical results with
extensive numerical simulations.
3 Derivation of the Fokker-Planck equation
We are interested in characterizing theprobability distribution for the point
γ(t) at the tip of the trace in the ensemble provided by different realizations
oftheSLEstochastic process. Eq. (2)impliesthenthatweshouldstudythe
inversemapg−1. However, thisisratherdifficult, sincethemapg−1 satisfies
t t
a partial differential equation instead of an ODE. There is a way out which
is rather well known and has been successfully used before [13, 14, 19]. It
happens that one needs to consider the backward time evolution:
2
∂ f (w) = , f (w) = w. (10)
t t 0
−f (w) ξ(t)
t
−
The relation of the original Loewner evolution (1) and the backward one
(10) in the stochastic setting is as follows. If ξ(t) is a symmetric (in time)
process with independent identically distributed increments, which is the
case for a L´evy process, then it is easy to show that for any fixed time t the
solution f (w) of the backward equation (10) has the same distribution as
t
g−1(w ξ(t))+ξ(t), see Refs. [13, 19]. Using the symbol =d for equality of
t −
distributions for random variables, we can write
f (w) =d g−1(w ξ(t))+ξ(t). (11)
t t −
It is useful to introduce a shifted conformal map
h (z) = g (z) ξ(t), (12)
t t
−
for which the Loewner equation acquires the Langevin-like form:
2
∂ h (z) = ∂ ξ(t), h (z) = z, (13)
t t t 0
h (z) −
t
8
assuming that ξ vanishes at t = 0. The first term is a deterministic drift
and the second — a random noise. The tip γ(t) is now mapped to zero, and
this can be taken as the definition of the tip. More formally,
γ(t) = lim h−1(w) (14)
t
w→0
where the limit is taken in the upper half plane.
In terms of the shifted map the equality of distributions (11) can be
written as
f (w) ξ(t) =d h−1(w). (15)
t − t
The left hand side z f (w) ξ(t) of this equation satisfies the Langevin-
t t
≡ −
like equation
2
∂ z = ∂ ξ(t), z = w, (16)
t t t 0
−z −
t
and in particular, if we set w = 0 in this equation, the resulting stochastic
dynamics should be the same as that of the tip of the trace γ(t).
Before weconvert theLangevin-like equation (16) toourmain analytical
tool, the corresponding Fokker-Planck equation, let us review again the
correspondence between the forward and backward flows and illustrate it
with figures. Equation (13) describes a flow in which w = h (z) follows a
t t
trajectory ofaparticleinthew plane,z beingitsinitialposition. Separating
the real and imaginary parts of w = u +iv , we get a system of coupled
t t t
equations
2u
t
∂ u = ∂ ξ(t), u = x,
t t u2+v2 − t 0
t t
2v
t
∂ v = , v = y, (17)
t t −u2+v2 0
t t
describing such a trajectory. As in many modern versions of dynamics, all
initial conditions and hence all trajectories are considered at the same time,
forming an ensemble. Two such trajectories are presented on the top panel
in Fig. 3. For a generic initial point z the trajectory w goes to infinity
t
in the horizontal u direction, while the vertical coordinate v monotonously
t
decreases. However, if the initial point happens to be a point γ(T) on the
SLE trace, the forward trajectory hits the origin in the mathematical plane
exactly at time T.
Conversely,wecanfixapointwinthemathematicalplaneandfollowthe
motion of its image z underthemap f (w) ξ(t) in thephysical plane, with
t t
−
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Figure 3: Top panel: two trajectories in the forward flow. Bottom panel:
thecorrespondingtrajectories in thebackward flow. Curvedarrows indicate
the flow of time. In both cases the flow trajectories are shown in grey, while
the black line represents an SLE trace. The trajectories on the bottom
panel have been produced with the same noise realization as used for the
forward evolution, reversed in time as described in the main text. The grey
trajectories in the top and bottom panel are thus identical.
the initial condition z = w. In components z = x +iy , the trajectories of
0 t t t
this backward flow satisfy the system of equations
2x
t
∂ x = ∂ ξ(t), x = u,
t t −x2+y2 − t 0
t t
2y
t
∂ y = , y = v. (18)
t t x2+y2 0
t t
The two trajectories shown on the bottom panel in Fig. 3 precisely retrace
the trajectories of the forward flow shown on the top panel. This has been
achieved by driving the backward evolution (18) by the time reversed noise
d
ξ(T t) ξ(T) = ξ(t) as compared to the forward evolution. In this case
− −
the final point z of the trajectory that started at the origin coincides with
T
the tip of the trace γ(T) at that time, but the rest of the trajectory does
not follow the SLE trace. If we drive the backward flow by an independent
10