RANDOM WALKS from STATISTICAL PHYSICS II Two Dimensons and Conformal Invariance Wald Lectures 2011 Joint Statistical Meetings
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RANDOM WALKS FROM STATISTICAL PHYSICS II Two Dimensons and Conformal Invariance Wald Lectures 2011 Joint Statistical Meetings Gregory F. Lawler Department of Mathematics Department of Statistics University of Chicago August 3, 2011 1 / 32 CRITICAL PHENOMENA IN STATISTICAL PHYSICS I Study systems at or near parameters at which a phase transition occurs I Parameter ¯ = C=T where T = temperature I Large ¯ (low temperature) | long range correlation. I Small ¯ (high temperature) | short range correlation I Critical value ¯c at which sharp transition occurs I Belief: systems at criticality \in the scaling limit" exhibit fractal-like behavior (power-law correlations) with nontrivial critical exponents. I The exponents depend on dimension. 2 / 32 TWO DIMENSIONS I Belavin, Polyakov, Zamolodchikov (1984) | critical systems in two dimensions in the scaling limit exhibit some kind of \conformal invariance". I A number of theoretical physicists (Nienhuis, Cardy, Duplantier, Saleur, ...) made predictions about critical exponents using nonrigorous methods | conformal ¯eld theory and Coulomb gas techniques. I Exact rational values for critical exponents | predictions strongly supported by numerical simulations I While much of the mathematical framework of conformal ¯eld theory was precise and rigorous (or rigorizable), the nature of the limit and the relation of the ¯eld theory to the lattice models was not well understood. 3 / 32 SELF-AVOIDING WALK (SAW) I Model for polymer chains | polymers are formed by monomers that are attached randomly except for a self-avoidance constraint. 2 ! = [!0;:::;!n];!j 2 Z ; j!j = n j!j ¡ !j¡1j = 1; j = 1;:::; n !j 6= !k ; 0 · j < k · n: I Critical exponent º: a typical SAW has diameter about j!jº. I If no self-avoidance constraint º = 1=2; for 2-d SAW Flory predicted º = 3=4. 4 / 32 N z w 0 N Each SAW from z to w gets measure e¡¯j!j: Partition function X Z = Z(N; ¯) = e¡¯j!j: ¯ small | typical path is two-dimensional ¯ large | typical path is one-dimensional ¯c | typical path is (1=º)-dimensional 5 / 32 Choose ¯ = ¯c ; let N ! 1; divide by Z(N; ¯) and hope to get a probability measure on curves connecting boundary points of the square. z w 6 / 32 N z w 0 N Similarly, if we ¯x D ½ C, we can consider walks restricted to the domain D z w Predict that these probability measures are conformally invariant. 7 / 32 SIMPLE RANDOM WALK N z w 0 N I Simple random walk | no self-avoidance constraint. Criticality: each walk ! gets weight (1=4)j!j. I Scaling limit is Brownian motion which is conformally invariant (L¶evy). 8 / 32 LOOP-ERASED RANDOM WALK Start with simple random walks and erase loops in chronological order to get a path with no self-intersections. Limit should be a measure on paths with no self-intersections. z w 9 / 32 CRITICAL PERCOLATION Color vertices of the triangular lattice in the upper half plane black or white independently each with probability 1=2. Put a boundary condition of black on negative real axis and white on positive real axis. The percolation exploration process is the boundary between black and white. 10 / 32 CARDY'S FORMULA The probability of a black crossing at criticality (in the limit as lattice space goes to zero) was predicted to be a conformal invariant. 1 1 A1 D A 3 x 1 Cardy used conformal ¯eld theory to predict the value. it is most easily given for an equilateral triangle. 11 / 32 STRATEGY I Make precise the conformal invariance assumption and other properties expected of scaling limit. I Find all possible limits satisfying these assumptions. I For a given discrete process, identify which is the correct limit. I Prove the discrete converges to continuous. Nonrigorous approaches in mathematical physics using conformal ¯eld theory had some of the properties of this strategy. 12 / 32 ASSUMPTIONS ON SCALING LIMIT Probability measure ¹D (z; w) on curves connecting boundary points of a domain D. f z w f(z) f(w) I Conformal invariance: If f is a conformal transformation f ± ¹D (z; w) = ¹f (D)(f (z); f (w)): I For simply connected D, it su±ces to know ¹H(0; 1) (Riemann mapping theorem). 13 / 32 I Domain Markov property Given γ[0; t], the conditional distribution on γ[t; 1) is the same as ¹Hnγ(0;t](γ(t); 1): γ (t) I Satis¯ed on discrete level by SAW, LERW, percolation exploration, Ising exploration ... (but not by simple random walk) 14 / 32 LOEWNER DIFFERENTIAL EQUATION I Developed by Loewner in studying Bieberbach conjecture I Let D be a simply connected proper domain in C containing 0. Riemann mapping theorem says there is a unique conformal transformation f : D ! D with f (0) = 0; f 0(0) > 0. P I 1 j f (z) = j=1 aj z I Bieberbach conjecture: jaj j · j a1 I Su±ces to prove for slit domains D = C n γ[t; 1) where γ(s) ! 1 as s ! 1 I Di®erential equation studies how coe±cients change as function of t I Loewner proved ja3j · 3 a1. BC proved by de Branges (proof uses Loewner di® eq) 15 / 32 LOEWNER EQUATION IN UPPER HALF PLANE I Let γ : (0; 1) ! H be a simple curve with γ(0+) = 0 and γ(t) ! 1 as t ! 1. I gt : H n γ(0; t] ! H g γ(t) t 0 Ut I Can reparametrize if necessary so that 2t g (z) = z + + ¢ ¢ ¢ ; z ! 1 t z I gt satis¯es 2 @t gt (z) = ; g0(z) = z: gt (z) ¡ Ut Moreover, Ut = gt (γ(t)) is continuous. 16 / 32 (Schramm) Suppose γ is a random curve satisfying conformal invariance and Domain Markov property. Then Ut must be a random continuous curve satisfying I For every s < t, Ut ¡ Us is independent of Ur ; 0 · r · s and has the same distribution as Ut¡s . ¡1 I c Uc2t has the same distribution as Ut . p Therefore, Ut = · Bt where Bt is a standard (one-dimensional) Brownian motion. The (chordal) Schramm-Loewner evolution with parameter · p (SLE·) is the solution obtained by choosing Ut = · Bt . 17 / 32 (Rohde-Schramm) Solving the Loewner equation with a Brownian input gives a random curve. The qualitative behavior of the curves varies greatly with · I 0 < · · 4 | simple (non self intersecting) curve I 4 < · < 8 | self-intersections (but not crossing); not plane-¯lling I 8 · · < 1 | plane-¯lling (Be®ara) For · < 8, the Hausdor® dimension of the paths is · 1 + : 8 18 / 32 The fundamental tools for studying SLE are those of stochastic calculus (It^ointegral and formula, martingales, Girsanov transformation) For which · does SLE have double points? Equivalent to ask, for which · does SLE hit the real line? Let x > 0 and Xt = Xt (x) = gt (x) ¡ Ut . Then SLE hits [x; 1) if and only if Xt reaches zero in ¯nite time. Xt satis¯es 2 p dXt = dt + · dBt : Xt Bessel equation. Well known that Xt reaches zero if and only if · > 4. 19 / 32 iθ What is the probability that z = re lies to the left of the SLE· curve? I Function only of θ, f (θ) with f (0) = 0; f (¼) = 1. I Let Mt be the probability given γ(0; t]. Then Mt = f (arg(gt (z) ¡ Ut )) is a martingale, i.e., if s < t, E [Mt j γ(0; s]] = Ms : I It^o'sformula gives a di®erential equation for f f 00(θ) + 2(1 ¡ 2a) f 0(θ) cot θ = 0; a = 2=·: I Z θ c dx f (θ) = 2¡4a : 0 sin x 20 / 32 WHICH · FOR WHICH MODEL? How does the measure of a path change when we perturb the boundary? (· · 4) w D D'\D γ z nc o 1fγ ½ Dg exp ¤(D0; γ; D0 n D) 2 J(D0; γ; D0 n γ) is a conformal invariant given by the measure (using a certain Brownian loop measure) of loops in D0 that intersect both D0 n D and γ. 21 / 32 I For SAW, perturbing the domain does not change the measure. Expect c = 0. I For LERW, shrinking the domain loses some simple random walks whose loop-erasure is γ. Expect c < 0. I c is the central charge which is the parameter used in conformal ¯eld theory to distinguish models. p (3· ¡ 8) (6 ¡ ·) (13 ¡ c) § (13 ¡ c)2 ¡ 144 c = ;· = : 2· 3 Each c < 1 corresponds to two values ·; ·0 with ··0 = 16. c = 1 corresponds to the double root · = ·0 = 4. 22 / 32 BROWNIAN PATHS I The ¯rst major problem solved with SLE was the Brownian intersection exponents. One example goes back to a conjecture of Mandelbrot. Consider a \Brownian island" formed by taking taking a Brownian motion (random walk), conditioning to end at the same place it began, and ¯lling in the bounded holds. Mandelbrot noted that simulations of this coastline indicated that it should have dimension 4=3. I L. showed that the dimension could be calculated in terms of a particular value of the intersection exponents. I L, Schramm, and Werner showed how the locality property of SLE6 could be used to calculate this exponent and veri¯ed Mandelbrot's conjecture. 23 / 32 CRITICAL PERCOLATION I Smirnov proved that the scaling limit of critical percolation on the triangular lattice satis¯es Cardy's formula. Using this and the work of LSW he established that the scaling limit of percolation is SLE6. I · = 6 is the only value of · for which SLE satis¯es the locality property | something that would be expected of the scaling limit of percolation.