Cardy embedding of random planar maps Nina Holden ETH Z urich, - - PowerPoint PPT Presentation

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Cardy embedding of random planar maps Nina Holden ETH Z urich, - - PowerPoint PPT Presentation

Cardy embedding of random planar maps Nina Holden ETH Z urich, Institute for Theoretical Studies Collaboration with Xin Sun. Based on our joint works with Bernardi, Garban, Gwynne, Lawler, Li, and Sep ulveda. August 1, 2019 Holden (ETH


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Cardy embedding of random planar maps

Nina Holden

ETH Z¨ urich, Institute for Theoretical Studies

Collaboration with Xin Sun. Based on our joint works with Bernardi, Garban, Gwynne, Lawler, Li, and Sep´ ulveda. August 1, 2019

Holden (ETH Z¨ urich) August 1, 2019 1 / 20

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Two random surfaces

random planar map (RPM) Liouville quantum gravity (LQG)

Holden (ETH Z¨ urich) August 1, 2019 2 / 20

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Planar maps

A planar map M is a finite connected graph drawn in the sphere, viewed up to continuous deformations.

Holden (ETH Z¨ urich) August 1, 2019 3 / 20

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Planar maps

A planar map M is a finite connected graph drawn in the sphere, viewed up to continuous deformations.

=

Holden (ETH Z¨ urich) August 1, 2019 3 / 20

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Planar maps

A planar map M is a finite connected graph drawn in the sphere, viewed up to continuous deformations.

=

Holden (ETH Z¨ urich) August 1, 2019 3 / 20

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Planar maps

A planar map M is a finite connected graph drawn in the sphere, viewed up to continuous deformations. A triangulation is a planar map where all faces have three edges.

=

Holden (ETH Z¨ urich) August 1, 2019 3 / 20

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Planar maps

A planar map M is a finite connected graph drawn in the sphere, viewed up to continuous deformations. A triangulation is a planar map where all faces have three edges. Given n ∈ N let M be a uniformly chosen triangulation with n vertices.

=

Holden (ETH Z¨ urich) August 1, 2019 3 / 20

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Planar maps

A planar map M is a finite connected graph drawn in the sphere, viewed up to continuous deformations. A triangulation is a planar map where all faces have three edges. Given n ∈ N let M be a uniformly chosen triangulation with n vertices. Enumeration results by Tutte and Mullin in 60’s.

=

Holden (ETH Z¨ urich) August 1, 2019 3 / 20

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The Gaussian free field (GFF)

Hamiltonian H(f ) quantifies how much f deviates from being harmonic H(f ) = 1 2

  • x∼y

(f (x) − f (y))2, f : 1 n Z2 ∩ [0, 1]2 → R.

1 1

1 n

Holden (ETH Z¨ urich) August 1, 2019 4 / 20

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The Gaussian free field (GFF)

Hamiltonian H(f ) quantifies how much f deviates from being harmonic H(f ) = 1 2

  • x∼y

(f (x) − f (y))2, f : 1 n Z2 ∩ [0, 1]2 → R. Discrete Gaussian free field (GFF) hn : 1

nZ2 ∩ [0, 1]2 → R is a random function

with hn|∂[0,1]2 = 0 and probability density rel. to Lebesgue measure proportional to exp(−H(hn)). n = 20, n = 100

Holden (ETH Z¨ urich) August 1, 2019 4 / 20

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The Gaussian free field (GFF)

Hamiltonian H(f ) quantifies how much f deviates from being harmonic H(f ) = 1 2

  • x∼y

(f (x) − f (y))2, f : 1 n Z2 ∩ [0, 1]2 → R. Discrete Gaussian free field (GFF) hn : 1

nZ2 ∩ [0, 1]2 → R is a random function

with hn|∂[0,1]2 = 0 and probability density rel. to Lebesgue measure proportional to exp(−H(hn)). hn(z) ∼ N(0, 2

π log n + O(1)) and Cov(hn(z), hn(w)) = − 2 π log |z − w| + O(1).

n = 20, n = 100

Holden (ETH Z¨ urich) August 1, 2019 4 / 20

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The Gaussian free field (GFF)

Hamiltonian H(f ) quantifies how much f deviates from being harmonic H(f ) = 1 2

  • x∼y

(f (x) − f (y))2, f : 1 n Z2 ∩ [0, 1]2 → R. Discrete Gaussian free field (GFF) hn : 1

nZ2 ∩ [0, 1]2 → R is a random function

with hn|∂[0,1]2 = 0 and probability density rel. to Lebesgue measure proportional to exp(−H(hn)). hn(z) ∼ N(0, 2

π log n + O(1)) and Cov(hn(z), hn(w)) = − 2 π log |z − w| + O(1).

The Gaussian free field h is the limit of hn when n → ∞. n = 20, n = 100

Holden (ETH Z¨ urich) August 1, 2019 4 / 20

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The Gaussian free field (GFF)

Hamiltonian H(f ) quantifies how much f deviates from being harmonic H(f ) = 1 2

  • x∼y

(f (x) − f (y))2, f : 1 n Z2 ∩ [0, 1]2 → R. Discrete Gaussian free field (GFF) hn : 1

nZ2 ∩ [0, 1]2 → R is a random function

with hn|∂[0,1]2 = 0 and probability density rel. to Lebesgue measure proportional to exp(−H(hn)). hn(z) ∼ N(0, 2

π log n + O(1)) and Cov(hn(z), hn(w)) = − 2 π log |z − w| + O(1).

The Gaussian free field h is the limit of hn when n → ∞. The GFF is a random distribution (i.e., random generalized function). n = 20, n = 100

Holden (ETH Z¨ urich) August 1, 2019 4 / 20

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Liouville quantum gravity (LQG)

If h : [0, 1]2 → R smooth and γ ∈ (0, 2), then eγh(dx2 + dy 2) defines the metric tensor of a Riemannian manifold.

Holden (ETH Z¨ urich) August 1, 2019 5 / 20

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Liouville quantum gravity (LQG)

If h : [0, 1]2 → R smooth and γ ∈ (0, 2), then eγh(dx2 + dy 2) defines the metric tensor of a Riemannian manifold. γ-Liouville quantum gravity (LQG): h is the Gaussian free field.

Holden (ETH Z¨ urich) August 1, 2019 5 / 20

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Liouville quantum gravity (LQG)

If h : [0, 1]2 → R smooth and γ ∈ (0, 2), then eγh(dx2 + dy 2) defines the metric tensor of a Riemannian manifold. γ-Liouville quantum gravity (LQG): h is the Gaussian free field. The definition does not make literal sense, since h is not a function. discrete GFF

Holden (ETH Z¨ urich) August 1, 2019 5 / 20

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Liouville quantum gravity (LQG)

If h : [0, 1]2 → R smooth and γ ∈ (0, 2), then eγh(dx2 + dy 2) defines the metric tensor of a Riemannian manifold. γ-Liouville quantum gravity (LQG): h is the Gaussian free field. The definition does not make literal sense, since h is not a function. Area measure eγhd2z and metric defined via regularized versions hǫ of h: µ(U) = lim

ǫ→0 ǫγ2/2

  • U

eγhǫ(z)d2z, U ⊂ [0, 1]2, d(z, w) = lim

ǫ→0 cǫ

inf

P:z→w

  • P

eγhǫ(z)/d dz, z, w ∈ [0, 1]2 (2019). discrete GFF LQG area

Holden (ETH Z¨ urich) August 1, 2019 5 / 20

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Liouville quantum gravity (LQG)

If h : [0, 1]2 → R smooth and γ ∈ (0, 2), then eγh(dx2 + dy 2) defines the metric tensor of a Riemannian manifold. γ-Liouville quantum gravity (LQG): h is the Gaussian free field. The definition does not make literal sense, since h is not a function. Area measure eγhd2z and metric defined via regularized versions hǫ of h: µ(U) = lim

ǫ→0 ǫγ2/2

  • U

eγhǫ(z)d2z, U ⊂ [0, 1]2, d(z, w) = lim

ǫ→0 cǫ

inf

P:z→w

  • P

eγhǫ(z)/d dz, z, w ∈ [0, 1]2 (2019). discrete GFF LQG area

Holden (ETH Z¨ urich) August 1, 2019 5 / 20

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Liouville quantum gravity (LQG)

If h : [0, 1]2 → R smooth and γ ∈ (0, 2), then eγh(dx2 + dy 2) defines the metric tensor of a Riemannian manifold. γ-Liouville quantum gravity (LQG): h is the Gaussian free field. The definition does not make literal sense, since h is not a function. Area measure eγhd2z and metric defined via regularized versions hǫ of h: µ(U) = lim

ǫ→0 ǫγ2/2

  • U

eγhǫ(z)d2z, U ⊂ [0, 1]2, d(z, w) = lim

ǫ→0 cǫ

inf

P:z→w

  • P

eγhǫ(z)/d dz, z, w ∈ [0, 1]2 (2019). The area measure is non-atomic and any open set has positive mass a.s., but the measure is a.s. singular with respect to Lebesgue measure. discrete GFF LQG area

Holden (ETH Z¨ urich) August 1, 2019 5 / 20

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Random planar maps converge to LQG

Two models for random surfaces: Random planar maps (RPM) Liouville quantum gravity (LQG) .

Holden (ETH Z¨ urich) August 1, 2019 6 / 20

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Random planar maps converge to LQG

Two models for random surfaces: Random planar maps (RPM) Liouville quantum gravity (LQG) . Conjectural relationship used by physicists to predict/calculate the dimension of random fractals and exponents of statistical physics models.

1 n

v

P[4 arms from v] ≈ n−5/4

Holden (ETH Z¨ urich) August 1, 2019 6 / 20

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Random planar maps converge to LQG

Two models for random surfaces: Random planar maps (RPM) Liouville quantum gravity (LQG) . Conjectural relationship used by physicists to predict/calculate the dimension of random fractals and exponents of statistical physics models. . What does it mean for a RPM to converge? Metric structure (Le Gall’13, Miermont’13) Conformal structure (H.-Sun’19) Statistical physics observables (Duplantier-Miller-Sheffield’14, ...)

Holden (ETH Z¨ urich) August 1, 2019 6 / 20

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Conformally embedded RPM converge to

  • 8/3-LQG

A B C embedding φ random planar map (RPM) Mn embedded random planar map

T

scaling limit

I ⇒

  • 8/3-LQG h

Cardy

Uniform triangulation Mn with n vertices, boundary length ⌈√n⌉.

Holden (ETH Z¨ urich) August 1, 2019 7 / 20

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Conformally embedded RPM converge to

  • 8/3-LQG

A B C embedding φ random planar map (RPM) Mn embedded random planar map

T

scaling limit

I ⇒

  • 8/3-LQG h

Cardy

Uniform triangulation Mn with n vertices, boundary length ⌈√n⌉. Cardy embedding: uses properties of percolation on the RPM.

Holden (ETH Z¨ urich) August 1, 2019 7 / 20

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Conformally embedded RPM converge to

  • 8/3-LQG

A B C embedding φ random planar map (RPM) Mn embedded random planar map

T

scaling limit

I ⇒

  • 8/3-LQG h

Cardy

Uniform triangulation Mn with n vertices, boundary length ⌈√n⌉. Cardy embedding: uses properties of percolation on the RPM. Let µn be renormalized counting measure on the vertices in T.

Holden (ETH Z¨ urich) August 1, 2019 7 / 20

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Conformally embedded RPM converge to

  • 8/3-LQG

A B C embedding φ random planar map (RPM) Mn embedded random planar map

T

scaling limit

I ⇒

  • 8/3-LQG h

Cardy

Uniform triangulation Mn with n vertices, boundary length ⌈√n⌉. Cardy embedding: uses properties of percolation on the RPM. Let µn be renormalized counting measure on the vertices in T. Let dn be a metric (distance function) on T prop. to graph distances.

Holden (ETH Z¨ urich) August 1, 2019 7 / 20

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Conformally embedded RPM converge to

  • 8/3-LQG

A B C embedding φ random planar map (RPM) Mn embedded random planar map

T

scaling limit

I ⇒

  • 8/3-LQG h

Cardy

Uniform triangulation Mn with n vertices, boundary length ⌈√n⌉. Cardy embedding: uses properties of percolation on the RPM. Let µn be renormalized counting measure on the vertices in T. Let dn be a metric (distance function) on T prop. to graph distances. Let µ be

  • 8/3-LQG area measure in T, and d the associated metric.

Holden (ETH Z¨ urich) August 1, 2019 7 / 20

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Conformally embedded RPM converge to

  • 8/3-LQG

A B C embedding φ random planar map (RPM) Mn embedded random planar map

T

scaling limit

I ⇒

  • 8/3-LQG h

Cardy

Uniform triangulation Mn with n vertices, boundary length ⌈√n⌉. Cardy embedding: uses properties of percolation on the RPM. Let µn be renormalized counting measure on the vertices in T. Let dn be a metric (distance function) on T prop. to graph distances. Let µ be

  • 8/3-LQG area measure in T, and d the associated metric.

Theorem (H.-Sun’19)

In the above setting, (µn, dn) ⇒ (µ, d).

Holden (ETH Z¨ urich) August 1, 2019 7 / 20

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Conformally embedded RPM converge to

  • 8/3-LQG

A B C embedding φ random planar map (RPM) Mn embedded random planar map

T

scaling limit

I ⇒

  • 8/3-LQG h

Cardy

Theorem (H.-Sun’19)

In the above setting, (µn, dn) ⇒ (µ, d). More precisely, ∃ coupling of Mn and h s.t. with probability 1, as n → ∞,

  • fdµn →
  • fdµ ∀ continuous f : T → [0, 1] (measure convergence)

dn(z, w) → d(z, w), uniformly in z, w ∈ T (metric convergence)

Holden (ETH Z¨ urich) August 1, 2019 7 / 20

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The Schramm-Loewner evolution (SLE)

One-parameter family of random fractal curves indexed by κ ≥ 0, which describe the scaling limit of statistical physics models

loop-erased random walk, κ = 2 Ising, κ = 3, and FK-Ising, κ = 16/3 percolation, κ = 6 discrete Gaussian free field level line, κ = 4 uniform spanning tree, κ = 8 SLE0 SLE2 SLE4

Holden (ETH Z¨ urich) August 1, 2019 8 / 20

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The Schramm-Loewner evolution (SLE)

One-parameter family of random fractal curves indexed by κ ≥ 0, which describe the scaling limit of statistical physics models

loop-erased random walk, κ = 2 Ising, κ = 3, and FK-Ising, κ = 16/3 percolation, κ = 6 discrete Gaussian free field level line, κ = 4 uniform spanning tree, κ = 8

Holden (ETH Z¨ urich) August 1, 2019 8 / 20

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The Schramm-Loewner evolution (SLE)

One-parameter family of random fractal curves indexed by κ ≥ 0, which describe the scaling limit of statistical physics models

loop-erased random walk, κ = 2 Ising, κ = 3, and FK-Ising, κ = 16/3 percolation, κ = 6 discrete Gaussian free field level line, κ = 4 uniform spanning tree, κ = 8

Holden (ETH Z¨ urich) August 1, 2019 8 / 20

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The Schramm-Loewner evolution (SLE)

One-parameter family of random fractal curves indexed by κ ≥ 0, which describe the scaling limit of statistical physics models

loop-erased random walk, κ = 2 Ising, κ = 3, and FK-Ising, κ = 16/3 percolation, κ = 6 discrete Gaussian free field level line, κ = 4 uniform spanning tree, κ = 8

Introduced by Schramm’99: SLE uniquely characterized by conformal invariance and domain Markov property.

Holden (ETH Z¨ urich) August 1, 2019 8 / 20

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Percolation on uniform triangulations ⇒ SLE6

A B C B C A

ηn ηn

Smirnov’01, Camia-Newman’06: ηn ⇒ SLE6 on triangular lattice. H.-Sun’19: ηn ⇒ SLE6 on Cardy embedded triangulation in a quenched sense.

Holden (ETH Z¨ urich) August 1, 2019 9 / 20

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Percolation on uniform triangulations ⇒ CLE6

A B C B C A

Γn Γn

The conformal loop ensemble (CLE6) is the loop version of SLE6. Smirnov’01, Camia-Newman’06: Γn ⇒ CLE6 on triangular lattice. H.-Sun’19: Γn ⇒ CLE6 on Cardy embedded triangulation.

Holden (ETH Z¨ urich) August 1, 2019 10 / 20

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Convergence of percolation crossing probability

Let Mn be a uniformly chosen triangulation with n (resp. ⌈√n⌉) inner (resp. boundary) vertices.

Holden (ETH Z¨ urich) August 1, 2019 11 / 20

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Convergence of percolation crossing probability

Let Mn be a uniformly chosen triangulation with n (resp. ⌈√n⌉) inner (resp. boundary) vertices. Pick edges an, bn, cn, dn uniformly at random from ∂Mn.

an bn cn dn

Holden (ETH Z¨ urich) August 1, 2019 11 / 20

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Convergence of percolation crossing probability

Let Mn be a uniformly chosen triangulation with n (resp. ⌈√n⌉) inner (resp. boundary) vertices. Pick edges an, bn, cn, dn uniformly at random from ∂Mn. Let Pn = Pn(Mn, an, bn, cn, dn) ∈ [0, 1] denote the probability of a blue crossing from anbn to cndn.

an bn cn dn

Pn =

an bn cn dn

Holden (ETH Z¨ urich) August 1, 2019 11 / 20

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Convergence of percolation crossing probability

Let Mn be a uniformly chosen triangulation with n (resp. ⌈√n⌉) inner (resp. boundary) vertices. Pick edges an, bn, cn, dn uniformly at random from ∂Mn. Let Pn = Pn(Mn, an, bn, cn, dn) ∈ [0, 1] denote the probability of a blue crossing from anbn to cndn. The random variable Pn converges in law as n → ∞.

an bn cn dn

Pn =

an bn cn dn

Holden (ETH Z¨ urich) August 1, 2019 11 / 20

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Convergence of percolation crossing probability

Let Mn be a uniformly chosen triangulation with n (resp. ⌈√n⌉) inner (resp. boundary) vertices. Pick edges an, bn, cn, dn uniformly at random from ∂Mn. Let Pn = Pn(Mn, an, bn, cn, dn) ∈ [0, 1] denote the probability of a blue crossing from anbn to cndn. The random variable Pn converges in law as n → ∞. Pn gives some notion of extremal distance between anbn and cndn.

an bn cn dn

Pn =

an bn cn dn

Holden (ETH Z¨ urich) August 1, 2019 11 / 20

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Cardy embedding: percolation-based embedding

A B C Cardy embedding φ random planar map embedded random planar map T

Holden (ETH Z¨ urich) August 1, 2019 12 / 20

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Cardy embedding: percolation-based embedding

A B C Cardy embedding φ random planar map embedded random planar map T a b c

Holden (ETH Z¨ urich) August 1, 2019 12 / 20

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Cardy embedding: percolation-based embedding

What is the “correct” position of v in T?

v b c a

Holden (ETH Z¨ urich) August 1, 2019 12 / 20

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Cardy embedding: percolation-based embedding

What is the “correct” position of v in T?

A B C x blue crossing

Holden (ETH Z¨ urich) August 1, 2019 12 / 20

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Cardy embedding: percolation-based embedding

What is the “correct” position of v in T?

A B C x blue crossing

pA(x) = P B C A x

Holden (ETH Z¨ urich) August 1, 2019 12 / 20

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Cardy embedding: percolation-based embedding

What is the “correct” position of v in T? Map v ∈ V (M) to x ∈ T such that (pA(x), pB(x), pC(x)) = ( pa(v), pb(v), pc(v)).

pA(x) = P B C A x

v b c a

  • pa(v) = P

Holden (ETH Z¨ urich) August 1, 2019 12 / 20

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RPM ⇒ LQG under conformal embedding

A B C embedding φ random planar map (RPM) Mn embedded random planar map

T

scaling limit

I ⇒

  • 8/3-LQG h

Cardy

Our result is for uniform triangulations and the Cardy embedding, but is also believed to hold for other

1 conformal embeddings, 2 local map constraints, and 3 universality classes of random planar maps. Holden (ETH Z¨ urich) August 1, 2019 13 / 20

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Discrete conformal embeddings

Circle packing Riemann uniformization Tutte embedding Cardy embedding circle packing (sphere topology) circle packing (disk topology)

Holden (ETH Z¨ urich) August 1, 2019 14 / 20

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Discrete conformal embeddings

Circle packing Riemann uniformization Tutte embedding Cardy embedding

Random planar map Riemannian manifold

Holden (ETH Z¨ urich) August 1, 2019 14 / 20

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Discrete conformal embeddings

Circle packing Riemann uniformization Tutte embedding Cardy embedding . Uniformization theorem: For any simply connected Riemann surface M there is a conformal map φ from M to either D, C or S2.

φ

Holden (ETH Z¨ urich) August 1, 2019 14 / 20

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Discrete conformal embeddings

Circle packing Riemann uniformization Tutte embedding Cardy embedding Tutte embedding

Holden (ETH Z¨ urich) August 1, 2019 14 / 20

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Conformally embedded RPM converge to

  • 8/3-LQG

A B C embedding φ random planar map (RPM) Mn embedded random planar map

T

scaling limit

I ⇒

  • 8/3-LQG h

Cardy

Holden (ETH Z¨ urich) August 1, 2019 15 / 20

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Conformally embedded RPM converge to

  • 8/3-LQG

The proof is based on multiple works, including: Percolation on triangulations: a bijective path to Liouville quantum gravity (Bernardi-H.-Sun) Minkowski content of Brownian cut points (Lawler-Li-H.-Sun) Natural parametrization of percolation interface and pivotal points (Li-H.-Sun) Uniform triangulations with simple boundary converge to the Brownian disk (Albenque-H.-Sun) Joint scaling limit of site percolation on random triangulations in the metric and peanosphere sense (Gwynne-H.-Sun) Liouville dynamical percolation (Garban-H.-Sep´ ulveda-Sun) Convergence of uniform triangulations under the Cardy embedding (H.-Sun)

Holden (ETH Z¨ urich) August 1, 2019 16 / 20

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Convergence as metric measure space

Mn is a uniform triangulation with n vertices and bdy length ⌈√n⌉.

Holden (ETH Z¨ urich) August 1, 2019 17 / 20

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Convergence as metric measure space

Mn is a uniform triangulation with n vertices and bdy length ⌈√n⌉. Mn is a random metric measure space.

n−1/4

Holden (ETH Z¨ urich) August 1, 2019 17 / 20

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Convergence as metric measure space

Mn is a uniform triangulation with n vertices and bdy length ⌈√n⌉. Mn is a random metric measure space. Gromov-Hausdorff-Prokhorov (GHP) topology on the space of metric measure spaces.

n−1/4

Holden (ETH Z¨ urich) August 1, 2019 17 / 20

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Convergence as metric measure space

Mn is a uniform triangulation with n vertices and bdy length ⌈√n⌉. Mn is a random metric measure space. Gromov-Hausdorff-Prokhorov (GHP) topology on the space of metric measure spaces.

Theorem (Albenque-H.-Sun’19)

Mn ⇒ M in the GHP topology, where M is

  • 8/3-LQG (equivalently, the

Brownian disk).

Building on Le Gall’13, Miermont’13, Bettinelli–Miermont’17, Poulalhon–Schaeffer’06, Addario-Berry–Albenque’17, Addario-Berry–Wen’17

n−1/4

Holden (ETH Z¨ urich) August 1, 2019 17 / 20

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Convergence as metric measure space with loops

Theorem (Albenque-H.-Sun’19)

Mn ⇒ M in the GHP topology, where M is

  • 8/3-LQG (the Brownian disk).

Holden (ETH Z¨ urich) August 1, 2019 18 / 20

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Convergence as metric measure space with loops

Theorem (Albenque-H.-Sun’19)

Mn ⇒ M in the GHP topology, where M is

  • 8/3-LQG (the Brownian disk).

Let Pn be a uniform percolation on Mn.

Holden (ETH Z¨ urich) August 1, 2019 18 / 20

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Convergence as metric measure space with loops

Theorem (Albenque-H.-Sun’19)

Mn ⇒ M in the GHP topology, where M is

  • 8/3-LQG (the Brownian disk).

Let Pn be a uniform percolation on Mn. Gromov-Hausdorff-Prokhorov-uniform (GHPU) topology on the space of metric measure spaces with a collection of loops.

Holden (ETH Z¨ urich) August 1, 2019 18 / 20

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Convergence as metric measure space with loops

Theorem (Albenque-H.-Sun’19)

Mn ⇒ M in the GHP topology, where M is

  • 8/3-LQG (the Brownian disk).

Let Pn be a uniform percolation on Mn. Gromov-Hausdorff-Prokhorov-uniform (GHPU) topology on the space of metric measure spaces with a collection of loops.

Theorem (Gwynne-H.-Sun’19)

(Mn, Pn) ⇒ (M, Γ) in the GHPU topology, where Γ is the conformal loop ensemble CLE6. Building on Gwynne-Miller’17, Bernardi-H.-Sun’18

Holden (ETH Z¨ urich) August 1, 2019 18 / 20

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Liouville dynamical percolation

Dynamical percolation (Pt)t≥0 on M: Each vertex has an exponential clock and its color is resampled when its clock rings.

Holden (ETH Z¨ urich) August 1, 2019 19 / 20

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Liouville dynamical percolation

Dynamical percolation (Pt)t≥0 on M: Each vertex has an exponential clock and its color is resampled when its clock rings.

Holden (ETH Z¨ urich) August 1, 2019 19 / 20

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Liouville dynamical percolation

Dynamical percolation (Pt)t≥0 on M: Each vertex has an exponential clock and its color is resampled when its clock rings.

Holden (ETH Z¨ urich) August 1, 2019 19 / 20

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Liouville dynamical percolation

Dynamical percolation (Pt)t≥0 on M: Each vertex has an exponential clock and its color is resampled when its clock rings.

Holden (ETH Z¨ urich) August 1, 2019 19 / 20

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Liouville dynamical percolation

Dynamical percolation (Pt)t≥0 on M: Each vertex has an exponential clock and its color is resampled when its clock rings.

Holden (ETH Z¨ urich) August 1, 2019 19 / 20

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SLIDE 67

Liouville dynamical percolation

Dynamical percolation (Pt)t≥0 on M: Each vertex has an exponential clock and its color is resampled when its clock rings.

Holden (ETH Z¨ urich) August 1, 2019 19 / 20

slide-68
SLIDE 68

Liouville dynamical percolation

Dynamical percolation (Pt)t≥0 on M: Each vertex has an exponential clock and its color is resampled when its clock rings. (Pn−1/4t)t≥0 ⇒ (Γt)t≥0, for (Γt)t≥0 Liouville dynamical percolation. Γt is a CLE6 for each t ≥ 0.

Holden (ETH Z¨ urich) August 1, 2019 19 / 20

slide-69
SLIDE 69

Liouville dynamical percolation

Dynamical percolation (Pt)t≥0 on M: Each vertex has an exponential clock and its color is resampled when its clock rings. (Pn−1/4t)t≥0 ⇒ (Γt)t≥0, for (Γt)t≥0 Liouville dynamical percolation. Γt is a CLE6 for each t ≥ 0.

Holden (ETH Z¨ urich) August 1, 2019 19 / 20

slide-70
SLIDE 70

Liouville dynamical percolation

Dynamical percolation (Pt)t≥0 on M: Each vertex has an exponential clock and its color is resampled when its clock rings. (Pn−1/4t)t≥0 ⇒ (Γt)t≥0, for (Γt)t≥0 Liouville dynamical percolation. Γt is a CLE6 for each t ≥ 0.

Holden (ETH Z¨ urich) August 1, 2019 19 / 20

slide-71
SLIDE 71

Liouville dynamical percolation

Dynamical percolation (Pt)t≥0 on M: Each vertex has an exponential clock and its color is resampled when its clock rings. (Pn−1/4t)t≥0 ⇒ (Γt)t≥0, for (Γt)t≥0 Liouville dynamical percolation. Γt is a CLE6 for each t ≥ 0.

Holden (ETH Z¨ urich) August 1, 2019 19 / 20

slide-72
SLIDE 72

Liouville dynamical percolation

Dynamical percolation (Pt)t≥0 on M: Each vertex has an exponential clock and its color is resampled when its clock rings. (Pn−1/4t)t≥0 ⇒ (Γt)t≥0, for (Γt)t≥0 Liouville dynamical percolation. Γt is a CLE6 for each t ≥ 0.

Holden (ETH Z¨ urich) August 1, 2019 19 / 20

slide-73
SLIDE 73

Liouville dynamical percolation

Dynamical percolation (Pt)t≥0 on M: Each vertex has an exponential clock and its color is resampled when its clock rings. (Pn−1/4t)t≥0 ⇒ (Γt)t≥0, for (Γt)t≥0 Liouville dynamical percolation. Γt is a CLE6 for each t ≥ 0.

Holden (ETH Z¨ urich) August 1, 2019 19 / 20

slide-74
SLIDE 74

Liouville dynamical percolation

Dynamical percolation (Pt)t≥0 on M: Each vertex has an exponential clock and its color is resampled when its clock rings. (Pn−1/4t)t≥0 ⇒ (Γt)t≥0, for (Γt)t≥0 Liouville dynamical percolation. Γt is a CLE6 for each t ≥ 0.

Holden (ETH Z¨ urich) August 1, 2019 19 / 20

slide-75
SLIDE 75

Liouville dynamical percolation

Dynamical percolation (Pt)t≥0 on M: Each vertex has an exponential clock and its color is resampled when its clock rings. (Pn−1/4t)t≥0 ⇒ (Γt)t≥0, for (Γt)t≥0 Liouville dynamical percolation. Γt is a CLE6 for each t ≥ 0.

Holden (ETH Z¨ urich) August 1, 2019 19 / 20

slide-76
SLIDE 76

Liouville dynamical percolation

Dynamical percolation (Pt)t≥0 on M: Each vertex has an exponential clock and its color is resampled when its clock rings. (Pn−1/4t)t≥0 ⇒ (Γt)t≥0, for (Γt)t≥0 Liouville dynamical percolation. Γt is a CLE6 for each t ≥ 0. (Γt)t≥0 is mixing (in particular, ergodic): Γt is asymptotically indep. of Γ0. limt→∞ Cov(E1(Γ0), E2(Γt)) = 0 for all events E1, E2.

Holden (ETH Z¨ urich) August 1, 2019 19 / 20

slide-77
SLIDE 77

Liouville dynamical percolation

Dynamical percolation (Pt)t≥0 on M: Each vertex has an exponential clock and its color is resampled when its clock rings. (Pn−1/4t)t≥0 ⇒ (Γt)t≥0, for (Γt)t≥0 Liouville dynamical percolation. Γt is a CLE6 for each t ≥ 0. (Γt)t≥0 is mixing (in particular, ergodic): Γt is asymptotically indep. of Γ0. limt→∞ Cov(E1(Γ0), E2(Γt)) = 0 for all events E1, E2. Noise sensitivity: If a fraction Cn−1/4 of the vertices are resampled for C ≫ 1, we get an essentially independent limiting CLE6.

Holden (ETH Z¨ urich) August 1, 2019 19 / 20

slide-78
SLIDE 78

Liouville dynamical percolation

Dynamical percolation (Pt)t≥0 on M: Each vertex has an exponential clock and its color is resampled when its clock rings. (Pn−1/4t)t≥0 ⇒ (Γt)t≥0, for (Γt)t≥0 Liouville dynamical percolation. Γt is a CLE6 for each t ≥ 0. (Γt)t≥0 is mixing (in particular, ergodic): Γt is asymptotically indep. of Γ0. limt→∞ Cov(E1(Γ0), E2(Γt)) = 0 for all events E1, E2. Noise sensitivity: If a fraction Cn−1/4 of the vertices are resampled for C ≫ 1, we get an essentially independent limiting CLE6. Corollary: k indep. percolations on a map M give k indep. CLE6’s in the scaling limit ⇒ convergence of Cardy embedding of M via LLN argument. (M, P) (M, P)

Holden (ETH Z¨ urich) August 1, 2019 19 / 20

slide-79
SLIDE 79

Liouville dynamical percolation

Dynamical percolation (Pt)t≥0 on M: Each vertex has an exponential clock and its color is resampled when its clock rings. (Pn−1/4t)t≥0 ⇒ (Γt)t≥0, for (Γt)t≥0 Liouville dynamical percolation. Γt is a CLE6 for each t ≥ 0. (Γt)t≥0 is mixing (in particular, ergodic): Γt is asymptotically indep. of Γ0. limt→∞ Cov(E1(Γ0), E2(Γt)) = 0 for all events E1, E2. Noise sensitivity: If a fraction Cn−1/4 of the vertices are resampled for C ≫ 1, we get an essentially independent limiting CLE6. Corollary: k indep. percolations on a map M give k indep. CLE6’s in the scaling limit ⇒ convergence of Cardy embedding of M via LLN argument. (M, P) (M, P)

Holden (ETH Z¨ urich) August 1, 2019 19 / 20

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SLIDE 80

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Thanks!

Holden (ETH Z¨ urich) August 1, 2019 20 / 20