Geometric functionals of fractal percolation
Steffen WinterKarlsruhe Institute of Technology
joint work with Michael Klatt
Thermodynamic formalism –Applications to geometry and number theory
Bremen, July 2017
1
Finsterbergen – Koserow – Friedrichroda – Greifswald –
Tabarz –
Fractal Geometry and Stochastics 6
Bad Herrenalb (Black Forest)
30 Sept – 5 Oct 2018
Save the date!
Scientific committeeChristoph BandtKenneth FalconerJun KigamiMark PollicottMartina Zahle
Organizers:Uta FreibergBen HamblyMichael HinzSteffen Winter
2
Finsterbergen – Koserow – Friedrichroda – Greifswald – Tabarz –
Fractal Geometry and Stochastics 6
Bad Herrenalb (Black Forest)
30 Sept – 5 Oct 2018
Save the date!
Scientific committeeChristoph BandtKenneth FalconerJun KigamiMark PollicottMartina Zahle
Organizers:Uta FreibergBen HamblyMichael HinzSteffen Winter
2
Finsterbergen – Koserow – Friedrichroda – Greifswald – Tabarz –
Fractal Geometry and Stochastics
6
Bad Herrenalb (Black Forest)
30 Sept – 5 Oct 2018
Save the date!
Scientific committeeChristoph BandtKenneth FalconerJun KigamiMark PollicottMartina Zahle
Organizers:Uta FreibergBen HamblyMichael HinzSteffen Winter
2
Finsterbergen – Koserow – Friedrichroda – Greifswald – Tabarz –
Fractal Geometry and Stochastics 6
Bad Herrenalb (Black Forest)
30 Sept – 5 Oct 2018
Save the date!
Scientific committeeChristoph BandtKenneth FalconerJun KigamiMark PollicottMartina Zahle
Organizers:Uta FreibergBen HamblyMichael HinzSteffen Winter
2
Finsterbergen – Koserow – Friedrichroda – Greifswald – Tabarz –
Fractal Geometry and Stochastics 6
Bad Herrenalb (Black Forest)
30 Sept – 5 Oct 2018
Save the date!
Scientific committeeChristoph BandtKenneth FalconerJun KigamiMark PollicottMartina Zahle
Organizers:Uta FreibergBen HamblyMichael HinzSteffen Winter
2
Geometric functionals of fractal percolation
Steffen WinterKarlsruhe Institute of Technology
joint work with Michael Klatt
Thermodynamic formalism –Applications to geometry and number theory
Bremen, July 2017
3
Fractal percolation
Let J := [0, 1]2 ⊂ R2, M ∈ N, M ≥ 2 and p ∈ [0, 1].
1. Subdivide J into squares of sidelength 1/M and decide foreach of them independently whether it is kept (with prob. p)or discarded (with prob. 1− p).
For n = 2, 3, . . .:
n. Repeat step 1 for each of the squares kept in step n − 1.
J
Let F (n) be the union of the squares kept in the n-th step.
4
Fractal percolation
Let J := [0, 1]2 ⊂ R2, M ∈ N, M ≥ 2 and p ∈ [0, 1].
1. Subdivide J into squares of sidelength 1/M and decide foreach of them independently whether it is kept (with prob. p)or discarded (with prob. 1− p).
For n = 2, 3, . . .:
n. Repeat step 1 for each of the squares kept in step n − 1.
J
1M
Let F (n) be the union of the squares kept in the n-th step.
4
Fractal percolation
Let J := [0, 1]2 ⊂ R2, M ∈ N, M ≥ 2 and p ∈ [0, 1].
1. Subdivide J into squares of sidelength 1/M and decide foreach of them independently whether it is kept (with prob. p)or discarded (with prob. 1− p).
For n = 2, 3, . . .:
n. Repeat step 1 for each of the squares kept in step n − 1.
J
1M
Let F (n) be the union of the squares kept in the n-th step.
4
Fractal percolation
Let J := [0, 1]2 ⊂ R2, M ∈ N, M ≥ 2 and p ∈ [0, 1].
1. Subdivide J into squares of sidelength 1/M and decide foreach of them independently whether it is kept (with prob. p)or discarded (with prob. 1− p).
For n = 2, 3, . . .:
n. Repeat step 1 for each of the squares kept in step n − 1.
J
1M
Let F (n) be the union of the squares kept in the n-th step.
4
Fractal percolation
Let J := [0, 1]2 ⊂ R2, M ∈ N, M ≥ 2 and p ∈ [0, 1].
1. Subdivide J into squares of sidelength 1/M and decide foreach of them independently whether it is kept (with prob. p)or discarded (with prob. 1− p).
For n = 2, 3, . . .:
n. Repeat step 1 for each of the squares kept in step n − 1.
J
1M
Let F (n) be the union of the squares kept in the n-th step.
4
Fractal percolation
Let J := [0, 1]2 ⊂ R2, M ∈ N, M ≥ 2 and p ∈ [0, 1].
1. Subdivide J into squares of sidelength 1/M and decide foreach of them independently whether it is kept (with prob. p)or discarded (with prob. 1− p).
For n = 2, 3, . . .:
n. Repeat step 1 for each of the squares kept in step n − 1.
J
1M
Let F (n) be the union of the squares kept in the n-th step.
4
Fractal percolation
Let J := [0, 1]2 ⊂ R2, M ∈ N, M ≥ 2 and p ∈ [0, 1].
1. Subdivide J into squares of sidelength 1/M and decide foreach of them independently whether it is kept (with prob. p)or discarded (with prob. 1− p).
For n = 2, 3, . . .:
n. Repeat step 1 for each of the squares kept in step n − 1.
F (2)
1M
Let F (n) be the union of the squares kept in the n-th step.
4
Fractal percolation
Let J := [0, 1]2 ⊂ R2, M ∈ N, M ≥ 2 and p ∈ [0, 1].
1. Subdivide J into squares of sidelength 1/M and decide foreach of them independently whether it is kept (with prob. p)or discarded (with prob. 1− p).
For n = 2, 3, . . .:
n. Repeat step 1 for each of the squares kept in step n − 1.
F (2)
1M
Let F (n) be the union of the squares kept in the n-th step.4
Fractal percolationI J ⊇ F (1) ⊇ F (2) ⊇ . . .
I Fractal percolation (Mandelbrot percolation, canonicalcurdling) is the random compact subset of J given by
F :=∞⋂n=1
F (n).
p = 0.8 p = 0.9 p = 0.97
5
Fractal percolationI J ⊇ F (1) ⊇ F (2) ⊇ . . .I Fractal percolation (Mandelbrot percolation, canonical
curdling) is the random compact subset of J given by
F :=∞⋂n=1
F (n).
p = 0.8 p = 0.9 p = 0.97
5
Fractal percolationI J ⊇ F (1) ⊇ F (2) ⊇ . . .I Fractal percolation (Mandelbrot percolation, canonical
curdling) is the random compact subset of J given by
F :=∞⋂n=1
F (n).
p = 0.8 p = 0.9 p = 0.97
5
Some properties
Recall that F (in Rd) depends on M ≥ 2 and p ∈ [0, 1].
I [Mandelbrot 74; Chayes, Chayes, Durrett 88]:
I If Mdp ≤ 1, then F = ∅ almost surely.I If Mdp > 1, then P(F 6= ∅) > 0, and conditioned on F 6= ∅,
the Hausdorff dimension (and the Minkowski dimension) isalmost surely
dimF = D :=log(Mdp)
logM.
I There exists pc = pc(M) with 1/Md < pc < 1 such that
– for p < pc , F is a.s. totally disconnected (‘dustlike’);– for p ≥ pc , F percolates with positive probability.
I Bounds on pc (d = 2):
I [Chayes, Chayes, Durrett 88]: 1/√M ≤ pc(M) ≤ 0.9999
I [White 2000]: pc(2) > 0.8107I [Don 14]: 0.881 ≤ pc(2) ≤ 0.993 and 0.784 ≤ pc(3) ≤ 0.940.
I further properties and related models: Falconer and Grimmett,
Dekking and Meester , Orzechowski, Broman and Camia, Rams and Simon ...
6
Some properties
Recall that F (in Rd) depends on M ≥ 2 and p ∈ [0, 1].I [Mandelbrot 74; Chayes, Chayes, Durrett 88]:
I If Mdp ≤ 1, then F = ∅ almost surely.
I If Mdp > 1, then P(F 6= ∅) > 0, and conditioned on F 6= ∅,the Hausdorff dimension (and the Minkowski dimension) isalmost surely
dimF = D :=log(Mdp)
logM.
I There exists pc = pc(M) with 1/Md < pc < 1 such that
– for p < pc , F is a.s. totally disconnected (‘dustlike’);– for p ≥ pc , F percolates with positive probability.
I Bounds on pc (d = 2):
I [Chayes, Chayes, Durrett 88]: 1/√M ≤ pc(M) ≤ 0.9999
I [White 2000]: pc(2) > 0.8107I [Don 14]: 0.881 ≤ pc(2) ≤ 0.993 and 0.784 ≤ pc(3) ≤ 0.940.
I further properties and related models: Falconer and Grimmett,
Dekking and Meester , Orzechowski, Broman and Camia, Rams and Simon ...
6
Some properties
Recall that F (in Rd) depends on M ≥ 2 and p ∈ [0, 1].I [Mandelbrot 74; Chayes, Chayes, Durrett 88]:
I If Mdp ≤ 1, then F = ∅ almost surely.I If Mdp > 1, then P(F 6= ∅) > 0, and conditioned on F 6= ∅,
the Hausdorff dimension (and the Minkowski dimension) isalmost surely
dimF = D :=log(Mdp)
logM.
I There exists pc = pc(M) with 1/Md < pc < 1 such that
– for p < pc , F is a.s. totally disconnected (‘dustlike’);– for p ≥ pc , F percolates with positive probability.
I Bounds on pc (d = 2):
I [Chayes, Chayes, Durrett 88]: 1/√M ≤ pc(M) ≤ 0.9999
I [White 2000]: pc(2) > 0.8107I [Don 14]: 0.881 ≤ pc(2) ≤ 0.993 and 0.784 ≤ pc(3) ≤ 0.940.
I further properties and related models: Falconer and Grimmett,
Dekking and Meester , Orzechowski, Broman and Camia, Rams and Simon ...
6
Some properties
Recall that F (in Rd) depends on M ≥ 2 and p ∈ [0, 1].I [Mandelbrot 74; Chayes, Chayes, Durrett 88]:
I If Mdp ≤ 1, then F = ∅ almost surely.I If Mdp > 1, then P(F 6= ∅) > 0, and conditioned on F 6= ∅,
the Hausdorff dimension (and the Minkowski dimension) isalmost surely
dimF = D :=log(Mdp)
logM.
I There exists pc = pc(M) with 1/Md < pc < 1 such that
– for p < pc , F is a.s. totally disconnected (‘dustlike’);– for p ≥ pc , F percolates with positive probability.
I Bounds on pc (d = 2):
I [Chayes, Chayes, Durrett 88]: 1/√M ≤ pc(M) ≤ 0.9999
I [White 2000]: pc(2) > 0.8107I [Don 14]: 0.881 ≤ pc(2) ≤ 0.993 and 0.784 ≤ pc(3) ≤ 0.940.
I further properties and related models: Falconer and Grimmett,
Dekking and Meester , Orzechowski, Broman and Camia, Rams and Simon ...
6
Some properties
Recall that F (in Rd) depends on M ≥ 2 and p ∈ [0, 1].I [Mandelbrot 74; Chayes, Chayes, Durrett 88]:
I If Mdp ≤ 1, then F = ∅ almost surely.I If Mdp > 1, then P(F 6= ∅) > 0, and conditioned on F 6= ∅,
the Hausdorff dimension (and the Minkowski dimension) isalmost surely
dimF = D :=log(Mdp)
logM.
I There exists pc = pc(M) with 1/Md < pc < 1 such that– for p < pc , F is a.s. totally disconnected (‘dustlike’);
– for p ≥ pc , F percolates with positive probability.
I Bounds on pc (d = 2):
I [Chayes, Chayes, Durrett 88]: 1/√M ≤ pc(M) ≤ 0.9999
I [White 2000]: pc(2) > 0.8107I [Don 14]: 0.881 ≤ pc(2) ≤ 0.993 and 0.784 ≤ pc(3) ≤ 0.940.
I further properties and related models: Falconer and Grimmett,
Dekking and Meester , Orzechowski, Broman and Camia, Rams and Simon ...
6
Some properties
Recall that F (in Rd) depends on M ≥ 2 and p ∈ [0, 1].I [Mandelbrot 74; Chayes, Chayes, Durrett 88]:
I If Mdp ≤ 1, then F = ∅ almost surely.I If Mdp > 1, then P(F 6= ∅) > 0, and conditioned on F 6= ∅,
the Hausdorff dimension (and the Minkowski dimension) isalmost surely
dimF = D :=log(Mdp)
logM.
I There exists pc = pc(M) with 1/Md < pc < 1 such that– for p < pc , F is a.s. totally disconnected (‘dustlike’);– for p ≥ pc , F percolates with positive probability.
I Bounds on pc (d = 2):
I [Chayes, Chayes, Durrett 88]: 1/√M ≤ pc(M) ≤ 0.9999
I [White 2000]: pc(2) > 0.8107I [Don 14]: 0.881 ≤ pc(2) ≤ 0.993 and 0.784 ≤ pc(3) ≤ 0.940.
I further properties and related models: Falconer and Grimmett,
Dekking and Meester , Orzechowski, Broman and Camia, Rams and Simon ...
6
Some properties
Recall that F (in Rd) depends on M ≥ 2 and p ∈ [0, 1].I [Mandelbrot 74; Chayes, Chayes, Durrett 88]:
I If Mdp ≤ 1, then F = ∅ almost surely.I If Mdp > 1, then P(F 6= ∅) > 0, and conditioned on F 6= ∅,
the Hausdorff dimension (and the Minkowski dimension) isalmost surely
dimF = D :=log(Mdp)
logM.
I There exists pc = pc(M) with 1/Md < pc < 1 such that– for p < pc , F is a.s. totally disconnected (‘dustlike’);– for p ≥ pc , F percolates with positive probability.
I Bounds on pc (d = 2):
I [Chayes, Chayes, Durrett 88]: 1/√M ≤ pc(M) ≤ 0.9999
I [White 2000]: pc(2) > 0.8107I [Don 14]: 0.881 ≤ pc(2) ≤ 0.993 and 0.784 ≤ pc(3) ≤ 0.940.
I further properties and related models: Falconer and Grimmett,
Dekking and Meester , Orzechowski, Broman and Camia, Rams and Simon ...
6
Some properties
Recall that F (in Rd) depends on M ≥ 2 and p ∈ [0, 1].I [Mandelbrot 74; Chayes, Chayes, Durrett 88]:
I If Mdp ≤ 1, then F = ∅ almost surely.I If Mdp > 1, then P(F 6= ∅) > 0, and conditioned on F 6= ∅,
the Hausdorff dimension (and the Minkowski dimension) isalmost surely
dimF = D :=log(Mdp)
logM.
I There exists pc = pc(M) with 1/Md < pc < 1 such that– for p < pc , F is a.s. totally disconnected (‘dustlike’);– for p ≥ pc , F percolates with positive probability.
I Bounds on pc (d = 2):I [Chayes, Chayes, Durrett 88]: 1/
√M ≤ pc(M) ≤ 0.9999
I [White 2000]: pc(2) > 0.8107I [Don 14]: 0.881 ≤ pc(2) ≤ 0.993 and 0.784 ≤ pc(3) ≤ 0.940.
I further properties and related models: Falconer and Grimmett,
Dekking and Meester , Orzechowski, Broman and Camia, Rams and Simon ...
6
Some properties
Recall that F (in Rd) depends on M ≥ 2 and p ∈ [0, 1].I [Mandelbrot 74; Chayes, Chayes, Durrett 88]:
I If Mdp ≤ 1, then F = ∅ almost surely.I If Mdp > 1, then P(F 6= ∅) > 0, and conditioned on F 6= ∅,
the Hausdorff dimension (and the Minkowski dimension) isalmost surely
dimF = D :=log(Mdp)
logM.
I There exists pc = pc(M) with 1/Md < pc < 1 such that– for p < pc , F is a.s. totally disconnected (‘dustlike’);– for p ≥ pc , F percolates with positive probability.
I Bounds on pc (d = 2):I [Chayes, Chayes, Durrett 88]: 1/
√M ≤ pc(M) ≤ 0.9999
I [White 2000]: pc(2) > 0.8107
I [Don 14]: 0.881 ≤ pc(2) ≤ 0.993 and 0.784 ≤ pc(3) ≤ 0.940.
I further properties and related models: Falconer and Grimmett,
Dekking and Meester , Orzechowski, Broman and Camia, Rams and Simon ...
6
Some properties
Recall that F (in Rd) depends on M ≥ 2 and p ∈ [0, 1].I [Mandelbrot 74; Chayes, Chayes, Durrett 88]:
I If Mdp ≤ 1, then F = ∅ almost surely.I If Mdp > 1, then P(F 6= ∅) > 0, and conditioned on F 6= ∅,
the Hausdorff dimension (and the Minkowski dimension) isalmost surely
dimF = D :=log(Mdp)
logM.
I There exists pc = pc(M) with 1/Md < pc < 1 such that– for p < pc , F is a.s. totally disconnected (‘dustlike’);– for p ≥ pc , F percolates with positive probability.
I Bounds on pc (d = 2):I [Chayes, Chayes, Durrett 88]: 1/
√M ≤ pc(M) ≤ 0.9999
I [White 2000]: pc(2) > 0.8107I [Don 14]: 0.881 ≤ pc(2) ≤ 0.993 and 0.784 ≤ pc(3) ≤ 0.940.
I further properties and related models: Falconer and Grimmett,
Dekking and Meester , Orzechowski, Broman and Camia, Rams and Simon ...
6
Some properties
Recall that F (in Rd) depends on M ≥ 2 and p ∈ [0, 1].I [Mandelbrot 74; Chayes, Chayes, Durrett 88]:
I If Mdp ≤ 1, then F = ∅ almost surely.I If Mdp > 1, then P(F 6= ∅) > 0, and conditioned on F 6= ∅,
the Hausdorff dimension (and the Minkowski dimension) isalmost surely
dimF = D :=log(Mdp)
logM.
I There exists pc = pc(M) with 1/Md < pc < 1 such that– for p < pc , F is a.s. totally disconnected (‘dustlike’);– for p ≥ pc , F percolates with positive probability.
I Bounds on pc (d = 2):I [Chayes, Chayes, Durrett 88]: 1/
√M ≤ pc(M) ≤ 0.9999
I [White 2000]: pc(2) > 0.8107I [Don 14]: 0.881 ≤ pc(2) ≤ 0.993 and 0.784 ≤ pc(3) ≤ 0.940.
I further properties and related models: Falconer and Grimmett,
Dekking and Meester , Orzechowski, Broman and Camia, Rams and Simon ...
6
Expected intrinsic volumes
I F is a fractal (a self-similar random set).
I For each n ∈ N0, F (n) is polyconvex.
Intrinsic volumes arewell defined for F (n).
I Let r := 1/M. We are interested in the limits
Zk(F ) := limn→∞
rn(D−k)EVk(F (n)).
I compare with fractal curvatures [Zahle 11]:
Ck(F ) := limε↘0
εD−kEVk(Fε).
7
Expected intrinsic volumes
I F is a fractal (a self-similar random set).
I For each n ∈ N0, F (n) is polyconvex. Intrinsic volumes arewell defined for F (n).
I Let r := 1/M. We are interested in the limits
Zk(F ) := limn→∞
rn(D−k)EVk(F (n)).
I compare with fractal curvatures [Zahle 11]:
Ck(F ) := limε↘0
εD−kEVk(Fε).
7
Expected intrinsic volumes
I F is a fractal (a self-similar random set).
I For each n ∈ N0, F (n) is polyconvex. Intrinsic volumes arewell defined for F (n).
I Let r := 1/M. We are interested in the limits
Zk(F ) := limn→∞
rn(D−k)EVk(F (n)).
I compare with fractal curvatures [Zahle 11]:
Ck(F ) := limε↘0
εD−kEVk(Fε).
7
Expected intrinsic volumes
I F is a fractal (a self-similar random set).
I For each n ∈ N0, F (n) is polyconvex. Intrinsic volumes arewell defined for F (n).
I Let r := 1/M. We are interested in the limits
Zk(F ) := limn→∞
rn(D−k)EVk(F (n)).
I compare with fractal curvatures [Zahle 11]:
Ck(F ) := limε↘0
εD−kEVk(Fε).
7
Curvature measures
Theorem (Local Steiner formula) [Federer 59]
For any compact, convex set K ⊂ Rd , thereexist finite measures C0(K , ·), . . . ,Cd(K , ·)such that
ρε(K ,B) =d∑
k=0
εd−kκd−kCk(K ,B)
for all Borel sets B ⊆ K and ε ≥ 0.
B
K
ρε(K,B)
There exists an additive extension to polyconvex sets (i.e. to anyfinite union of convex sets) [Groemer 78]:
Ck(K ∪M, ·) = Ck(K , ·) + Ck(M, ·)− Ck(K ∩M, ·)
Vk(K ) := Ck(K ,Rd) k-th total curvature or intrinsic volume
8
Curvature measures
Theorem (Local Steiner formula) [Federer 59]
For any compact, convex set K ⊂ Rd , thereexist finite measures C0(K , ·), . . . ,Cd(K , ·)such that
ρε(K ,B) =d∑
k=0
εd−kκd−kCk(K ,B)
for all Borel sets B ⊆ K and ε ≥ 0.
B
K
ρε(K,B)
There exists an additive extension to polyconvex sets (i.e. to anyfinite union of convex sets) [Groemer 78]:
Ck(K ∪M, ·) = Ck(K , ·) + Ck(M, ·)− Ck(K ∩M, ·)
Vk(K ) := Ck(K ,Rd) k-th total curvature or intrinsic volume
8
Curvature measures
Theorem (Local Steiner formula) [Federer 59]
For any compact, convex set K ⊂ Rd , thereexist finite measures C0(K , ·), . . . ,Cd(K , ·)such that
ρε(K ,B) =d∑
k=0
εd−kκd−kCk(K ,B)
for all Borel sets B ⊆ K and ε ≥ 0.
B
K
ρε(K,B)
There exists an additive extension to polyconvex sets (i.e. to anyfinite union of convex sets) [Groemer 78]:
Ck(K ∪M, ·) = Ck(K , ·) + Ck(M, ·)− Ck(K ∩M, ·)
Vk(K ) := Ck(K ,Rd) k-th total curvature or intrinsic volume
8
Curvature measures
Theorem (Local Steiner formula) [Federer 59]
For any compact, convex set K ⊂ Rd , thereexist finite measures C0(K , ·), . . . ,Cd(K , ·)such that
ρε(K ,B) =d∑
k=0
εd−kκd−kCk(K ,B)
for all Borel sets B ⊆ K and ε ≥ 0.
B
K
ρε(K,B)
There exists an additive extension to polyconvex sets (i.e. to anyfinite union of convex sets) [Groemer 78]:
Ck(K ∪M, ·) = Ck(K , ·) + Ck(M, ·)− Ck(K ∩M, ·)
Vk(K ) := Ck(K ,Rd) k-th total curvature or intrinsic volume
8
Properties of curvature measures
I geometric interpretation:Vd – volumeVd−1 – surface areaVk – average k-volume of the projections to
k-dim. subspacesintegrals of mean curvature
V0 – Euler characteristic
I motion invariance (g Euclidean motion):
Vk(gK ) = Vk(K )
I homogeneity of degree k (λ > 0):
Vk(λK ) = λkVk(K )
I continuity (convex sets): Kn → K implies Vk(Kn)→ Vk(K )
I Hadwigers characterization theorem [Hadwiger 57]
9
Properties of curvature measures
I geometric interpretation:Vd – volumeVd−1 – surface areaVk – average k-volume of the projections to
k-dim. subspacesintegrals of mean curvature
V0 – Euler characteristic
I motion invariance (g Euclidean motion):
Vk(gK ) = Vk(K )
I homogeneity of degree k (λ > 0):
Vk(λK ) = λkVk(K )
I continuity (convex sets): Kn → K implies Vk(Kn)→ Vk(K )
I Hadwigers characterization theorem [Hadwiger 57]
9
Properties of curvature measures
I geometric interpretation:Vd – volumeVd−1 – surface areaVk – average k-volume of the projections to
k-dim. subspacesintegrals of mean curvature
V0 – Euler characteristic
I motion invariance (g Euclidean motion):
Vk(gK ) = Vk(K )
I homogeneity of degree k (λ > 0):
Vk(λK ) = λkVk(K )
I continuity (convex sets): Kn → K implies Vk(Kn)→ Vk(K )
I Hadwigers characterization theorem [Hadwiger 57]
9
Properties of curvature measures
I geometric interpretation:Vd – volumeVd−1 – surface areaVk – average k-volume of the projections to
k-dim. subspacesintegrals of mean curvature
V0 – Euler characteristic
I motion invariance (g Euclidean motion):
Vk(gK ) = Vk(K )
I homogeneity of degree k (λ > 0):
Vk(λK ) = λkVk(K )
I continuity (convex sets): Kn → K implies Vk(Kn)→ Vk(K )
I Hadwigers characterization theorem [Hadwiger 57]
9
Properties of curvature measures
I geometric interpretation:Vd – volumeVd−1 – surface areaVk – average k-volume of the projections to
k-dim. subspacesintegrals of mean curvature
V0 – Euler characteristic
I motion invariance (g Euclidean motion):
Vk(gK ) = Vk(K )
I homogeneity of degree k (λ > 0):
Vk(λK ) = λkVk(K )
I continuity (convex sets): Kn → K implies Vk(Kn)→ Vk(K )
I Hadwigers characterization theorem [Hadwiger 57]
9
Expected intrinsic volumes
Theorem (Existence of the limits)
Let F be fractal percolation in Rd with parameters M ≥ 2 andp ∈ [0, 1].
Then, for each k ∈ {0, . . . , d}, the limit
Zk(F ) = limn→∞
rn(D−k)EVk(F (n))
exists and is given by
Vk([0, 1]d) +∑
T⊂{1,...,Md},|T |≥2
(−1)|T |−1∞∑n=1
rn(D−k)EVk(⋂j∈T
F j(n)).
F j(n) is the union of the level-ncubes contained in Jj ∩ F (n).
J
1M
J1 J2 J3
J4 J5 J6
J9J8J7
10
Expected intrinsic volumes
Theorem (Existence of the limits)
Let F be fractal percolation in Rd with parameters M ≥ 2 andp ∈ [0, 1]. Then, for each k ∈ {0, . . . , d}, the limit
Zk(F ) = limn→∞
rn(D−k)EVk(F (n))
exists
and is given by
Vk([0, 1]d) +∑
T⊂{1,...,Md},|T |≥2
(−1)|T |−1∞∑n=1
rn(D−k)EVk(⋂j∈T
F j(n)).
F j(n) is the union of the level-ncubes contained in Jj ∩ F (n).
J
1M
J1 J2 J3
J4 J5 J6
J9J8J7
10
Expected intrinsic volumes
Theorem (Existence of the limits)
Let F be fractal percolation in Rd with parameters M ≥ 2 andp ∈ [0, 1]. Then, for each k ∈ {0, . . . , d}, the limit
Zk(F ) = limn→∞
rn(D−k)EVk(F (n))
exists and is given by
Vk([0, 1]d) +∑
T⊂{1,...,Md},|T |≥2
(−1)|T |−1∞∑n=1
rn(D−k)EVk(⋂j∈T
F j(n)).
F j(n) is the union of the level-ncubes contained in Jj ∩ F (n).
J
1M
J1 J2 J3
J4 J5 J6
J9J8J7
10
The case d = 2
In R2, the general formula reduces to
Z0(F ) =1− E1 − E2 + E3 − E4,
where
E1 := 2M(M − 1)∞∑n=1
rnD
E2 := 2(M − 1)2∞∑n=1
rnD
E3 := 4(M − 1)2∞∑n=1
rnD
E4 := (M − 1)2∞∑n=1
rnD
1 2
1
4
1 2
3
11
The case d = 2
In R2, the general formula reduces to
Z0(F ) =1− E1 − E2 + E3 − E4,
where
E1 := 2M(M − 1)∞∑n=1
rnDEV0(F 1(n) ∩ F 2(n))
E2 := 2(M − 1)2∞∑n=1
rnD
E3 := 4(M − 1)2∞∑n=1
rnD
E4 := (M − 1)2∞∑n=1
rnD
1 2
1
4
1 2
3
11
The case d = 2
In R2, the general formula reduces to
Z0(F ) =1− E1 − E2 + E3 − E4,
where
E1 := 2M(M − 1)∞∑n=1
rnDEV0(F 1(n) ∩ F 2(n))
E2 := 2(M − 1)2∞∑n=1
rnDEV0(F 1(n) ∩ F 4(n))
E3 := 4(M − 1)2∞∑n=1
rnD
E4 := (M − 1)2∞∑n=1
rnD
1 2
1
4
1 2
3
11
The case d = 2
In R2, the general formula reduces to
Z0(F ) =1− E1 − E2 + E3 − E4,
where
E1 := 2M(M − 1)∞∑n=1
rnDEV0(F 1(n) ∩ F 2(n))
E2 := 2(M − 1)2∞∑n=1
rnDEV0(F 1(n) ∩ F 4(n))
E3 := 4(M − 1)2∞∑n=1
rnDEV0
3⋂j=1
F j(n)
E4 := (M − 1)2∞∑n=1
rnD
1 2
1
4
1 2
3
11
The case d = 2
In R2, the general formula reduces to
Z0(F ) =1− E1 − E2 + E3 − E4,
where
E1 := 2M(M − 1)∞∑n=1
rnDEV0(F 1(n) ∩ F 2(n))
E2 := 2(M − 1)2∞∑n=1
rnDEV0(F 1(n) ∩ F 4(n))
E3 := 4(M − 1)2∞∑n=1
rnDEV0
3⋂j=1
F j(n)
E4 := (M − 1)2
∞∑n=1
rnDEV0
4⋂j=1
F j(n)
1 2
1
4
1 2
3
11
The case d = 2
In R2, the general formula reduces to
Z0(F ) =1− E1 − E2 + E3 − E4,
where
E1 := 2M(M − 1)∞∑n=1
rnDEV0(F 1(n) ∩ F 2(n))
E2 := 2(M − 1)2∞∑n=1
rnDp2n
E3 := 4(M − 1)2∞∑n=1
rnDEV0
3⋂j=1
F j(n)
E4 := (M − 1)2
∞∑n=1
rnDEV0
4⋂j=1
F j(n)
1 2
1
4
1 2
3
11
The case d = 2
In R2, the general formula reduces to
Z0(F ) =1− E1 − E2 + E3 − E4,
where
E1 := 2M(M − 1)∞∑n=1
rnDEV0(F 1(n) ∩ F 2(n))
E2 := 2(M − 1)2∞∑n=1
rnDp2n
E3 := 4(M − 1)2∞∑n=1
rnDp3n
E4 := (M − 1)2∞∑n=1
rnDEV0
4⋂j=1
F j(n)
1 2
1
4
1 2
3
11
The case d = 2
In R2, the general formula reduces to
Z0(F ) =1− E1 − E2 + E3 − E4,
where
E1 := 2M(M − 1)∞∑n=1
rnDEV0(F 1(n) ∩ F 2(n))
E2 := 2(M − 1)2∞∑n=1
rnDp2n
E3 := 4(M − 1)2∞∑n=1
rnDp3n
E4 := (M − 1)2∞∑n=1
rnDp4n
1 2
1
4
1 2
3
11
The case d = 2
In R2, the general formula reduces to
Z0(F ) =1− E1 − E2 + E3 − E4,
where
E1 := 2M(M − 1)∞∑n=1
rnDEV0(F 1(n) ∩ F 2(n))
E2 := 2(M − 1)2∞∑n=1
rnDp2n
E3 := 4(M − 1)2∞∑n=1
rnDp3n
E4 := (M − 1)2∞∑n=1
rnDp4n
1 2
1
4
1 2
3
11
Reducing the dimension
I Let K1, K2 be two independent (1-dim.) fractal percolationsdefined on I = [0, 1] (with the same M and p as F ).
I For n ∈ N, let Ki (n), i = 1, 2 denote the n-th steps of theirconstruction.
I Let Ki (n), i = 1, 2 be the random set, which equals Ki (n)with probability p and is empty otherwise.
Then for each n ∈ N, in distribution
F 1(n) ∩ F 2(n) = ψ(K1(n − 1) ∩ K2(n − 1)),
where ψ : I → R2 is the similarity mapping I to J1 ∩ J2.
F 1(n) F 2(n)
12
Reducing the dimension
I Let K1, K2 be two independent (1-dim.) fractal percolationsdefined on I = [0, 1] (with the same M and p as F ).
I For n ∈ N, let Ki (n), i = 1, 2 denote the n-th steps of theirconstruction.
I Let Ki (n), i = 1, 2 be the random set, which equals Ki (n)with probability p and is empty otherwise.
Then for each n ∈ N, in distribution
F 1(n) ∩ F 2(n) = ψ(K1(n − 1) ∩ K2(n − 1)),
where ψ : I → R2 is the similarity mapping I to J1 ∩ J2.
F 1(n) F 2(n)
12
Reducing the dimension
I Let K1, K2 be two independent (1-dim.) fractal percolationsdefined on I = [0, 1] (with the same M and p as F ).
I For n ∈ N, let Ki (n), i = 1, 2 denote the n-th steps of theirconstruction.
I Let Ki (n), i = 1, 2 be the random set, which equals Ki (n)with probability p and is empty otherwise.
Then for each n ∈ N, in distribution
F 1(n) ∩ F 2(n) = ψ(K1(n − 1) ∩ K2(n − 1)),
where ψ : I → R2 is the similarity mapping I to J1 ∩ J2.
F 1(n) F 2(n)
12
Reducing the dimension
I Let K1, K2 be two independent (1-dim.) fractal percolationsdefined on I = [0, 1] (with the same M and p as F ).
I For n ∈ N, let Ki (n), i = 1, 2 denote the n-th steps of theirconstruction.
I Let Ki (n), i = 1, 2 be the random set, which equals Ki (n)with probability p and is empty otherwise.
Then for each n ∈ N, in distribution
F 1(n) ∩ F 2(n) = ψ(K1(n − 1) ∩ K2(n − 1)),
where ψ : I → R2 is the similarity mapping I to J1 ∩ J2.
F 1(n) F 2(n)
12
Reducing the dimension
I Let K1, K2 be two independent (1-dim.) fractal percolationsdefined on I = [0, 1] (with the same M and p as F ).
I For n ∈ N, let Ki (n), i = 1, 2 denote the n-th steps of theirconstruction.
I Let Ki (n), i = 1, 2 be the random set, which equals Ki (n)with probability p and is empty otherwise.
Then for each n ∈ N, in distribution
F 1(n) ∩ F 2(n) = ψ(K1(n − 1) ∩ K2(n − 1)),
where ψ : I → R2 is the similarity mapping I to J1 ∩ J2.
In particular,
EVk(F 1(n) ∩ F 2(n)) = rkp2EVk(K1(n − 1) ∩ K2(n − 1)).
12
Expected Euler characteristic (d = 2)
Z0(F ) = 1− E1 + (M − 1)2(− 2p
M2 − p+
4p2
M2 − p2− p3
M2 − p3
)E1 = 2(M − 1)2
((3
M − 1− 4p
M − p+
p2
M − p2
)p
M − p
− 2Mp
(M − 1)(M2 − p)+
4Mp2
(M − p)(M2 − p2)− Mp3
(M − p2)(M2 − p3)
)
.
13
Expected Euler characteristic (d = 2)
Z0(F ) = 1− E1 + (M − 1)2(− 2p
M2 − p+
4p2
M2 − p2− p3
M2 − p3
)E1 = 2(M − 1)2
((3
M − 1− 4p
M − p+
p2
M − p2
)p
M − p
− 2Mp
(M − 1)(M2 − p)+
4Mp2
(M − p)(M2 − p2)− Mp3
(M − p2)(M2 − p3)
)
0
0.5
1
0 0.2 0.4 0.6 0.8 1
Z0
p
M=2
M=3
M=4
13
Expected Euler characteristic (d = 2)
Z0(F ) = 1− E1 + (M − 1)2(− 2p
M2 − p+
4p2
M2 − p2− p3
M2 − p3
)E1 = 2(M − 1)2
((3
M − 1− 4p
M − p+
p2
M − p2
)p
M − p
− 2Mp
(M − 1)(M2 − p)+
4Mp2
(M − p)(M2 − p2)− Mp3
(M − p2)(M2 − p3)
)
0
0.5
1
0 0.2 0.4 0.6 0.8 1
pcZ0
p
M=2
13
Expected Euler characteristic (d = 2)
Z0(F ) = 1− E1 + (M − 1)2(− 2p
M2 − p+
4p2
M2 − p2− p3
M2 − p3
)E1 = 2(M − 1)2
((3
M − 1− 4p
M − p+
p2
M − p2
)p
M − p
− 2Mp
(M − 1)(M2 − p)+
4Mp2
(M − p)(M2 − p2)− Mp3
(M − p2)(M2 − p3)
)
0
0.5
1
0 0.2 0.4 0.6 0.8 1
pcZ0
p
M=3
13
A lower bound for pc?Is the zero of the rescaled limit of expected Euler characteristics alower bound for pc?
Does Z0(Fp,M) > 0 imply p < pc?
Z0(Fp,M) := limn→∞
rDnV0(Fp,M(n))
It is well known [Chayes, Chayes 89] that limM→∞
pc(M) = psite , where
psite ≈ 0.5927.. is the percolation threshhold of site percolation onthe lattice Z2. The zero of f (p) := lim
M→∞Z0(Fp,M) stays even well
below psite,8 ≈ 0.407...
0
0.5
1
0 0.2 0.4 0.6 0.8 1
psite,8
Z0
p
10
100
M
14
A lower bound for pc?Is the zero of the rescaled limit of expected Euler characteristics alower bound for pc? Does Z0(Fp,M) > 0 imply p < pc?
Z0(Fp,M) := limn→∞
rDnV0(Fp,M(n))
It is well known [Chayes, Chayes 89] that limM→∞
pc(M) = psite , where
psite ≈ 0.5927.. is the percolation threshhold of site percolation onthe lattice Z2. The zero of f (p) := lim
M→∞Z0(Fp,M) stays even well
below psite,8 ≈ 0.407...
0
0.5
1
0 0.2 0.4 0.6 0.8 1
psite,8
Z0
p
10
100
M
14
A lower bound for pc?Is the zero of the rescaled limit of expected Euler characteristics alower bound for pc? Does Z0(Fp,M) > 0 imply p < pc?
Z0(Fp,M) := limn→∞
rDnV0(Fp,M(n))
It is well known [Chayes, Chayes 89] that limM→∞
pc(M) = psite , where
psite ≈ 0.5927.. is the percolation threshhold of site percolation onthe lattice Z2.
The zero of f (p) := limM→∞
Z0(Fp,M) stays even well
below psite,8 ≈ 0.407...
0
0.5
1
0 0.2 0.4 0.6 0.8 1
psite,8
Z0
p
10
100
M
14
A lower bound for pc?Is the zero of the rescaled limit of expected Euler characteristics alower bound for pc? Does Z0(Fp,M) > 0 imply p < pc?
Z0(Fp,M) := limn→∞
rDnV0(Fp,M(n))
It is well known [Chayes, Chayes 89] that limM→∞
pc(M) = psite , where
psite ≈ 0.5927.. is the percolation threshhold of site percolation onthe lattice Z2. The zero of f (p) := lim
M→∞Z0(Fp,M) stays even well
below psite,8 ≈ 0.407...
0
0.5
1
0 0.2 0.4 0.6 0.8 1
psite,8
Z0
p
10
100
M
14
A lower bound for pc?Is the zero of the rescaled limit of expected Euler characteristics alower bound for pc? Does Z0(Fp,M) > 0 imply p < pc?
Z0(Fp,M) := limn→∞
rDnV0(Fp,M(n))
It is well known [Chayes, Chayes 89] that limM→∞
pc(M) = psite , where
psite ≈ 0.5927.. is the percolation threshhold of site percolation onthe lattice Z2. The zero of f (p) := lim
M→∞Z0(Fp,M) stays even well
below psite,8 ≈ 0.407...
0
0.5
1
0 0.2 0.4 0.6 0.8 1
psite,8
Z0
p
10
100
M
14
Another approximation of FInstead of the sequence F (n) consider the union G (n) of thoseboxes Q of level n, which have a nonempty intersection with F .Then
I G (n) ⊆ F (n),
I F =⋂
n G (n);
I G (n) are still polyconvex;
I similar formulas hold for the rescaled limits of Vk(G (n));
I the survival probability q := q(p) := Pp(F 6= ∅) appears
q = (1− p + pq)M2
F (2)
1M
15
Another approximation of FInstead of the sequence F (n) consider the union G (n) of thoseboxes Q of level n, which have a nonempty intersection with F .Then
I G (n) ⊆ F (n),
I F =⋂
n G (n);
I G (n) are still polyconvex;
I similar formulas hold for the rescaled limits of Vk(G (n));
I the survival probability q := q(p) := Pp(F 6= ∅) appears
q = (1− p + pq)M2
F (2)
1M
15
Another approximation of FInstead of the sequence F (n) consider the union G (n) of thoseboxes Q of level n, which have a nonempty intersection with F .Then
I G (n) ⊆ F (n),
I F =⋂
n G (n);
I G (n) are still polyconvex;
I similar formulas hold for the rescaled limits of Vk(G (n));
I the survival probability q := q(p) := Pp(F 6= ∅) appears
q = (1− p + pq)M2
F (2)
1M
15
Another approximation of FInstead of the sequence F (n) consider the union G (n) of thoseboxes Q of level n, which have a nonempty intersection with F .Then
I G (n) ⊆ F (n),
I F =⋂
n G (n);
I G (n) are still polyconvex;
I similar formulas hold for the rescaled limits of Vk(G (n));
I the survival probability q := q(p) := Pp(F 6= ∅) appears
q = (1− p + pq)M2
F (2)
1M
15
Another approximation of FInstead of the sequence F (n) consider the union G (n) of thoseboxes Q of level n, which have a nonempty intersection with F .Then
I G (n) ⊆ F (n),
I F =⋂
n G (n);
I G (n) are still polyconvex;
I similar formulas hold for the rescaled limits of Vk(G (n));
I the survival probability q := q(p) := Pp(F 6= ∅) appears
q = (1− p + pq)M2
F (2)
1M
15
Another approximation of FInstead of the sequence F (n) consider the union G (n) of thoseboxes Q of level n, which have a nonempty intersection with F .Then
I G (n) ⊆ F (n),
I F =⋂
n G (n);
I G (n) are still polyconvex;
I similar formulas hold for the rescaled limits of Vk(G (n));
I the survival probability q := q(p) := Pp(F 6= ∅) appears
q = (1− p + pq)M2
F (2)
1M
15
Another approximation of FInstead of the sequence F (n) consider the union G (n) of thoseboxes Q of level n, which have a nonempty intersection with F .Then
I G (n) ⊆ F (n),I F =
⋂n G (n);
I G (n) are still polyconvex;I similar formulas hold for the rescaled limits of Vk(G (n));I the survival probability q := q(p) := Pp(F 6= ∅) appears
q = (1− p + pq)M2
Z0(F ) = ....1− E1 + (M − 1)2(− 2pq2
M2 − p+
4p2q3
M2 − p2− p3q4
M2 − p3
),
E1 = 2(M − 1)2q2((
3
M − 1− 4pq
M − p+
p2q2
M − p2
)p
M − p
− 2Mp
(M − 1)(M2 − p)+
4Mp2q
(M − p)(M2 − p2)− Mp3q2
(M − p2)(M2 − p3)
)15
Approximation by F (n) vs. G (n)
16
Approximation by F (n) vs. G (n)
16
Approximation by F (n) vs. G (n)
16
Yet another approximation of F
In F (n) diagonal connections are counted,which produce many extra holes.
But di-agonal connections do not survive in F .Consider the closed complements of F (n)in J = [0, 1]d :
C (n) = J \ F (n).
Then −V0(C (n)) corresponds to the Eu-ler characteristic of F (n) with a 4-neigh-borhood (no diagonal connections).
Yk(F ) := limn→∞
rn(D−k)EVk(C (n))
17
Yet another approximation of F
In F (n) diagonal connections are counted,which produce many extra holes. But di-agonal connections do not survive in F .
Consider the closed complements of F (n)in J = [0, 1]d :
C (n) = J \ F (n).
Then −V0(C (n)) corresponds to the Eu-ler characteristic of F (n) with a 4-neigh-borhood (no diagonal connections).
Yk(F ) := limn→∞
rn(D−k)EVk(C (n))
17
Yet another approximation of F
In F (n) diagonal connections are counted,which produce many extra holes. But di-agonal connections do not survive in F .Consider the closed complements of F (n)in J = [0, 1]d :
C (n) = J \ F (n).
Then −V0(C (n)) corresponds to the Eu-ler characteristic of F (n) with a 4-neigh-borhood (no diagonal connections).
Yk(F ) := limn→∞
rn(D−k)EVk(C (n))
17
Yet another approximation of F
In F (n) diagonal connections are counted,which produce many extra holes. But di-agonal connections do not survive in F .Consider the closed complements of F (n)in J = [0, 1]d :
C (n) = J \ F (n).
Then −V0(C (n)) corresponds to the Eu-ler characteristic of F (n) with a 4-neigh-borhood (no diagonal connections).
Yk(F ) := limn→∞
rn(D−k)EVk(C (n))
17
Yet another approximation of F
In F (n) diagonal connections are counted,which produce many extra holes. But di-agonal connections do not survive in F .Consider the closed complements of F (n)in J = [0, 1]d :
C (n) = J \ F (n).
Then −V0(C (n)) corresponds to the Eu-ler characteristic of F (n) with a 4-neigh-borhood (no diagonal connections).
Yk(F ) := limn→∞
rn(D−k)EVk(C (n))
17
Approximation by the complements
Theorem (Existence of the limits)
Let F be fractal percolation in Rd with parameters M ≥ 2 andp ∈ (1/Md , 1].
Then, for each k < D, the limit
Yk(F ) := limn→∞
rn(D−k)EVk(C (n))
exists and is given by
cd ,k +∑
T⊂{1,...,Md},|T |≥2
(−1)|T |−1∞∑n=1
rn(D−k)EVk(⋂j∈T
C j(n)),
where cd,k :=Md−k(1− p)
Md−kp − 1Vk([0, 1]d).
C j(n) is the union of the level-n cubescontained in Jj ∩ C (n).
J
1M
J1 J2 J3
J4 J5 J6
J9J8J7
18
Approximation by the complements
Theorem (Existence of the limits)
Let F be fractal percolation in Rd with parameters M ≥ 2 andp ∈ (1/Md , 1]. Then, for each k < D, the limit
Yk(F ) := limn→∞
rn(D−k)EVk(C (n))
exists
and is given by
cd ,k +∑
T⊂{1,...,Md},|T |≥2
(−1)|T |−1∞∑n=1
rn(D−k)EVk(⋂j∈T
C j(n)),
where cd,k :=Md−k(1− p)
Md−kp − 1Vk([0, 1]d).
C j(n) is the union of the level-n cubescontained in Jj ∩ C (n).
J
1M
J1 J2 J3
J4 J5 J6
J9J8J7
18
Approximation by the complements
Theorem (Existence of the limits)
Let F be fractal percolation in Rd with parameters M ≥ 2 andp ∈ (1/Md , 1]. Then, for each k < D, the limit
Yk(F ) := limn→∞
rn(D−k)EVk(C (n))
exists and is given by
cd ,k +∑
T⊂{1,...,Md},|T |≥2
(−1)|T |−1∞∑n=1
rn(D−k)EVk(⋂j∈T
C j(n)),
where cd,k :=Md−k(1− p)
Md−kp − 1Vk([0, 1]d).
C j(n) is the union of the level-n cubescontained in Jj ∩ C (n). J
1M
J1 J2 J3
J4 J5 J6
J9J8J7
18
The case d = 2
In R2 (and for k = 0), the general formula reduces to
Y0(F ) =M2(1− p)
M2p − 1− E1 − E2 + E3 − E4,
where
E1 := 2M(M − 1)∞∑n=1
rnD
E2 := 2(M − 1)2∞∑n=1
rnD
E3 := 4(M − 1)2∞∑n=1
rnD
E4 := (M − 1)2∞∑n=1
rnD
1 2
1
4
1 2
3
19
The case d = 2
In R2 (and for k = 0), the general formula reduces to
Y0(F ) =M2(1− p)
M2p − 1− E1 − E2 + E3 − E4,
where
E1 := 2M(M − 1)∞∑n=1
rnDEV0(C 1(n) ∩ C 2(n))
E2 := 2(M − 1)2∞∑n=1
rnD
E3 := 4(M − 1)2∞∑n=1
rnD
E4 := (M − 1)2∞∑n=1
rnD
1 2
1
4
1 2
3
19
The case d = 2
In R2 (and for k = 0), the general formula reduces to
Y0(F ) =M2(1− p)
M2p − 1− E1 − E2 + E3 − E4,
where
E1 := 2M(M − 1)∞∑n=1
rnDEV0(C 1(n) ∩ C 2(n))
E2 := 2(M − 1)2∞∑n=1
rnDEV0(C 1(n) ∩ C 4(n))
E3 := 4(M − 1)2∞∑n=1
rnD
E4 := (M − 1)2∞∑n=1
rnD
1 2
1
4
1 2
3
19
The case d = 2
In R2 (and for k = 0), the general formula reduces to
Y0(F ) =M2(1− p)
M2p − 1− E1 − E2 + E3 − E4,
where
E1 := 2M(M − 1)∞∑n=1
rnDEV0(C 1(n) ∩ C 2(n))
E2 := 2(M − 1)2∞∑n=1
rnDEV0(C 1(n) ∩ C 4(n))
E3 := 4(M − 1)2∞∑n=1
rnDEV0
3⋂j=1
C j(n)
E4 := (M − 1)2∞∑n=1
rnD
1 2
1
4
1 2
3
19
The case d = 2In R2 (and for k = 0), the general formula reduces to
Y0(F ) =M2(1− p)
M2p − 1− E1 − E2 + E3 − E4,
where
E1 := 2M(M − 1)∞∑n=1
rnDEV0(C 1(n) ∩ C 2(n))
E2 := 2(M − 1)2∞∑n=1
rnDEV0(C 1(n) ∩ C 4(n))
E3 := 4(M − 1)2∞∑n=1
rnDEV0
3⋂j=1
C j(n)
E4 := (M − 1)2
∞∑n=1
rnDEV0
4⋂j=1
C j(n)
1 2
1
4
1 2
3
19
The case d = 2In R2 (and for k = 0), the general formula reduces to
Y0(F ) =M2(1− p)
M2p − 1− E1 − E2 + E3 − E4,
where
E1 := 2M(M − 1)∞∑n=1
rnDEV0(C 1(n) ∩ C 2(n))
E2 := 2(M − 1)2∞∑n=1
rnD(1− pn)2
E3 := 4(M − 1)2∞∑n=1
rnDEV0
3⋂j=1
C j(n)
E4 := (M − 1)2
∞∑n=1
rnDEV0
4⋂j=1
C j(n)
1 2
1
4
1 2
3
19
The case d = 2
In R2 (and for k = 0), the general formula reduces to
Y0(F ) =M2(1− p)
M2p − 1− E1 − E2 + E3 − E4,
where
E1 := 2M(M − 1)∞∑n=1
rnDEV0(C 1(n) ∩ C 2(n))
E2 := 2(M − 1)2∞∑n=1
rnD(1− pn)2
E3 := 4(M − 1)2∞∑n=1
rnD(1− pn)3
E4 := (M − 1)2∞∑n=1
rnDEV0
4⋂j=1
C j(n)
1 2
1
4
1 2
3
19
The case d = 2
In R2 (and for k = 0), the general formula reduces to
Y0(F ) =M2(1− p)
M2p − 1− E1 − E2 + E3 − E4,
where
E1 := 2M(M − 1)∞∑n=1
rnDEV0(C 1(n) ∩ C 2(n))
E2 := 2(M − 1)2∞∑n=1
rnD(1− pn)2
E3 := 4(M − 1)2∞∑n=1
rnD(1− pn)3
E4 := (M − 1)2∞∑n=1
rnD(1− pn)4
1 2
1
4
1 2
3
19
The case d = 2
In R2 (and for k = 0), the general formula reduces to
Y0(F ) =M2(1− p)
M2p − 1− E1 − E2 + E3 − E4,
where
E1 := 2M(M − 1)∞∑n=1
rnDEV0(C 1(n) ∩ C 2(n))
E2 := 2(M − 1)2∞∑n=1
rnD(1− pn)2
E3 := 4(M − 1)2∞∑n=1
rnD(1− pn)3
E4 := (M − 1)2∞∑n=1
rnD(1− pn)4
1 2
1
4
1 2
3
19
Reducing the dimension
I Let K1, K2 be two independent (1-dim.) fractal percolationsdefined on I = [0, 1] (with the same M and p as F ).
I For n ∈ N, let Ki (n), i = 1, 2 denote the n-th steps of theirconstruction and set Di (n) := Ki (n)c .
I Then, for Ki (n) (the random set, which equals Ki (n) withprobability p and is empty otherwise), i = 1, 2, we had
F 1(n) ∩ F 2(n) = ψ(K1(n − 1) ∩ K2(n − 1)),
where ψ : I → R2 is the similarity mapping I to J1 ∩ J2.
I This implies
F 1(n) F 2(n)
20
Reducing the dimension
I Let K1, K2 be two independent (1-dim.) fractal percolationsdefined on I = [0, 1] (with the same M and p as F ).
I For n ∈ N, let Ki (n), i = 1, 2 denote the n-th steps of theirconstruction and set Di (n) := Ki (n)c .
I Then, for Ki (n) (the random set, which equals Ki (n) withprobability p and is empty otherwise), i = 1, 2, we had
F 1(n) ∩ F 2(n) = ψ(K1(n − 1) ∩ K2(n − 1)),
where ψ : I → R2 is the similarity mapping I to J1 ∩ J2.
I This implies
F 1(n) F 2(n)
20
Reducing the dimension
I Let K1, K2 be two independent (1-dim.) fractal percolationsdefined on I = [0, 1] (with the same M and p as F ).
I For n ∈ N, let Ki (n), i = 1, 2 denote the n-th steps of theirconstruction and set Di (n) := Ki (n)c .
I Then, for Ki (n) (the random set, which equals Ki (n) withprobability p and is empty otherwise), i = 1, 2, we had
F 1(n) ∩ F 2(n) = ψ(K1(n − 1) ∩ K2(n − 1)),
where ψ : I → R2 is the similarity mapping I to J1 ∩ J2.
I This implies
F 1(n) F 2(n)
20
Reducing the dimension
I Let K1, K2 be two independent (1-dim.) fractal percolationsdefined on I = [0, 1] (with the same M and p as F ).
I For n ∈ N, let Ki (n), i = 1, 2 denote the n-th steps of theirconstruction and set Di (n) := Ki (n)c .
I Then, for Ki (n) (the random set, which equals Ki (n) withprobability p and is empty otherwise), i = 1, 2, we had
F 1(n) ∩ F 2(n) = ψ(K1(n − 1) ∩ K2(n − 1)),
where ψ : I → R2 is the similarity mapping I to J1 ∩ J2.
I This implies
C 1(n) ∩ C 2(n) = ψ(D1(n − 1) ∩ D2(n − 1))
where Di (n) is the random set which equals Di (n) with probabilityp and I with probability 1− p.
20
Expected Euler characteristic (complements)
Y0(F ) =M2(1− p)
M2p − 1− E1
+ (M − 1)2(
1
M2p − 1− 4p
M2 − 1+
4p
M2 − p− p3
M2 − p3
),
E1 =2M(M − 1)
M − p
(2(1− p)
M − 1+
2(M − 1)p
M2 − p− p(1− p2)
M − p2
)− 2M(M − 1)2p3
(M − p2)(M2 − p3)+
2M(M − 1)
M2p − 1− 8M
M + 1+
4M(M − 1)p
M2 − p.
21
Expected Euler characteristic (complements)
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
2× 2
pc
p
Z0
Y0
22
Expected Euler characteristic (complements)
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
3× 3
pc
p
Z0
Y0
22
Expected Euler characteristic (complements)
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
3× 3
χ
p
n = 4
6
8
22
A lower bound for pc?
I Does Y0(Fp,M) > 0 imply p < pc?
Y0(Fp,M) := limn→∞
rDnV0(Fp,M(n))
I Recall that limM→∞
pc(M) = psite , where psite ≈ 0.5927...
0
0.5
1
0 0.2 0.4 0.6 0.8 1
psite,8 psite
Z0
p
10
100
M
What is the red limit curve? limM→∞−Y0(Fp,M) = 1− 2p + p3
23
A lower bound for pc?
I Does Y0(Fp,M) > 0 imply p < pc?
Y0(Fp,M) := limn→∞
rDnV0(Fp,M(n))
I Recall that limM→∞
pc(M) = psite , where psite ≈ 0.5927...
0
0.5
1
0 0.2 0.4 0.6 0.8 1
psite,8 psite
Z0
p
10
100
M
What is the red limit curve? limM→∞−Y0(Fp,M) = 1− 2p + p3
23
A lower bound for pc?
I Does Y0(Fp,M) > 0 imply p < pc?
Y0(Fp,M) := limn→∞
rDnV0(Fp,M(n))
I Recall that limM→∞
pc(M) = psite , where psite ≈ 0.5927...
0
0.5
1
0 0.2 0.4 0.6 0.8 1
psite,8 psite
Z0
p
10
100
M
What is the red limit curve?
limM→∞−Y0(Fp,M) = 1− 2p + p3
23
A lower bound for pc?
I Does Y0(Fp,M) > 0 imply p < pc?
Y0(Fp,M) := limn→∞
rDnV0(Fp,M(n))
I Recall that limM→∞
pc(M) = psite , where psite ≈ 0.5927...
0
0.5
1
0 0.2 0.4 0.6 0.8 1
psite,8 psite
Z0
p
10
100
M
What is the red limit curve? limM→∞−Y0(Fp,M) = 1− 2p + p3
23
Final remarks and open questions
I Is there a rigorous connection between of expected Eulercharacteristics and pc?
I What is the ‘right’ approximation of F?(F (n))n? (C (n))n? Parallel sets? ...?
I similar relations for other scale invariant models (Booleanmultiscale models, Brownian loop soup, ...) [Broman,Camia 10]
0
0.5
1
0 0.2 0.4 0.6 0.8 1
psite,8 psite
Z0
p
10
100
M
24
Final remarks and open questions
I Is there a rigorous connection between of expected Eulercharacteristics and pc?
I What is the ‘right’ approximation of F?(F (n))n? (C (n))n? Parallel sets? ...?
I similar relations for other scale invariant models (Booleanmultiscale models, Brownian loop soup, ...) [Broman,Camia 10]
0
0.5
1
0 0.2 0.4 0.6 0.8 1
psite,8 psite
Z0
p
10
100
M24
Final remarks and open questions
I Is there a rigorous connection between of expected Eulercharacteristics and pc?
I What is the ‘right’ approximation of F?(F (n))n? (C (n))n? Parallel sets? ...?
I similar relations for other scale invariant models (Booleanmultiscale models, Brownian loop soup, ...) [Broman,Camia 10]
0
0.5
1
0 0.2 0.4 0.6 0.8 1
psite,8 psite
Z0
p
10
100
M24
Final remarks and open questions
I Is there a rigorous connection between of expected Eulercharacteristics and pc?
I What is the ‘right’ approximation of F?(F (n))n? (C (n))n? Parallel sets? ...?
I similar relations for other scale invariant models (Booleanmultiscale models, Brownian loop soup, ...) [Broman,Camia 10]
0
0.5
1
0 0.2 0.4 0.6 0.8 1
psite,8 psite
Z0
p
10
100
M24