counting social interactions for discrete subsets of the
play

Counting social interactions for discrete subsets of the plane - PowerPoint PPT Presentation

Counting social interactions for discrete subsets of the plane Samantha Fairchild University of Washington skayf@uw.edu Overview The Golden L and holonomy vector population density 1 Counting closed geodesics via -orbits 2 Expected


  1. Counting social interactions for discrete subsets of the plane Samantha Fairchild University of Washington skayf@uw.edu

  2. Overview The Golden L and holonomy vector population density 1 Counting closed geodesics via Γ-orbits 2 Expected populations on n -street 3 Few nearby neighbors 4 BREAK 5 Higher moments of the Siegel–Veech transform 6 Proof ideas: Orbit decomposition and counting orbits 7

  3. The Golden L

  4. Holonomy Vectors on the Golden L

  5. Veech ’98 Set of closed geodesics are finite union of H 5 orbits. Λ 5 = H 5 · e 1 ⊔ H 5 · ue 1 √ u = 1 + 5 2 � � �� � � �� π 0 1 1 2 cos q H q = , − 1 0 0 1

  6. Looking at one orbit �� � � � � �� π 0 1 1 2 cos q V = H 5 · e 1 H q = , − 1 0 0 1

  7. Looking at one orbit � � �� � � �� π 0 1 1 2 cos V = H 5 · e 1 H q = , q − 1 0 0 1

  8. Our friend the Torus � � �� � � �� π 0 1 1 2 cos V = H 3 · e 1 H q = , q − 1 0 0 1

  9. Population Density on the torus Assuming Riemann Hypothesis (Wu, 2002) # { V ∩ B (0 , R ) } = 6 221 π 2 ( π R 2 ) + O ( R 304 + ǫ )

  10. Population density on the Golden L Theorem (BNRW 2019) # { V ∩ B (0 , R ) } = 10 4 3 π 2 · π R 2 + O ( R 3 )

  11. Population density on the Golden L Theorem (BNRW 2019) # { V ∩ B (0 , R ) } = 10 4 3 π 2 · π R 2 + O ( R 3 ) Theorem (Burrin-F., Coming soon!) Ω bounded Jordan measurable domain E (# { V ∩ R · Ω } ) = 10 3 π 2 · | Ω | R 2 + O ( R c ) where c = max { 4 3 , 2 s 1 } .

  12. Theorem (Burrin-F., Coming soon!) Ω bounded Jordan measurable domain E (# { V ∩ R · Ω } ) = 10 3 π 2 · | Ω | R 2 + O ( R c ) where c = max { 4 3 , 2 s 1 } . Big proof idea: Count pairs of vectors in V ! � � v 2 v 1 , w 2 Given v , w ∈ V ∩ B (0 , 30) with | v ∧ w | < 30 plot w 1

  13. Population density on n th street Counting Pairs by determinant (F. 2019) 3 π 2 · π 2 E ( { v , w ∈ V ∩ B (0 , R ) : | v ∧ w | = n } ) ∼ 10 n · ϕ ( n ) · R 2

  14. Population density on n th street

  15. Density of nearby neighbors Corollary to F.2019, Coming soon! For all δ > 0, there exists ǫ > 0 so that # { v ∈ V ∩ B (0 , R ) : ∃ w ∈ V ∩ B ( v , ǫ ) } lim sup < δ. R 2 R →∞

  16. Density of nearby neighbors Corollary to F.2019, Coming soon! For all δ > 0, there exists ǫ > 0 so that # { v ∈ V ∩ B (0 , R ) : ∃ w ∈ V ∩ B ( v , ǫ ) } lim sup < δ. R 2 R →∞ v , w ∈ V ∩ B (0 , 50) | v ∧ w | = 1 || w ∈ B ( v , 1 / 2)

  17. Break

  18. Siegel–Veech Integral Formula Γ < SL (2 , R ) non-uniform lattice Non-uniform : SL (2 , R ) / Γ not compact Lattice : Γ is discrete with c (Γ) def = vol( SL (2 , R ) / Γ) < ∞ . V = Γ · e 1

  19. Siegel–Veech Integral Formula Γ < SL (2 , R ) non-uniform lattice Non-uniform : SL (2 , R ) / Γ not compact Lattice : Γ is discrete with c (Γ) def = vol( SL (2 , R ) / Γ) < ∞ . V = Γ · e 1 Theorem (Veech ’98) For f ∈ B c ( R 2 ) define the Siegel–Veech transform � f : SL (2 , R ) / Γ → R � � f ( g ) = f ( gv ) v ∈ V

  20. Siegel–Veech Integral Formula Γ < SL (2 , R ) non-uniform lattice Non-uniform : SL (2 , R ) / Γ not compact Lattice : Γ is discrete with c (Γ) def = vol( SL (2 , R ) / Γ) < ∞ . V = Γ · e 1 Theorem (Veech ’98) For f ∈ B c ( R 2 ) define the Siegel–Veech transform � f : SL (2 , R ) / Γ → R � � f ( g ) = f ( gv ) v ∈ V the Siegel–Veech mean value formula � � 1 � f ( g ) d µ ( g ) = R 2 f ( x ) dx . c (Γ) SL (2 , R ) / Γ

  21. Siegel–Veech Integral Formula Theorem (Veech ’98) For f ∈ B c ( R 2 ) define the Siegel–Veech transform � f : SL (2 , R ) / Γ → R � � f ( g ) = f ( gv ) v ∈ V the Siegel–Veech mean value formula � � 1 � f ( g ) d µ ( g ) = R 2 f ( x ) dx . c (Γ) SL (2 , R ) / Γ 1 c (Γ) · π R 2 # { V ∩ B (0 , R ) } ∼

  22. Higher moments for general Γ Theorem (Fairchild ’19) � � � 2 ( g ) d µ ( g ) � f SL (2 , R ) / Γ � 1 = R 2 f ( x ) f ( x ) + f ( x ) f ( − x ) dx c (Γ) � � �� � � �� � � ϕ ( n ) 1 1 + f g f g d η ( g ) 0 c (Γ) n SL (2 , R ) n ∈ N (Γ)

  23. Theorem (Fairchild ’19) � � � 2 ( g ) d µ ( g ) � f SL (2 , R ) / Γ � 1 = R 2 f ( x ) f ( x ) + f ( x ) f ( − x ) dx c (Γ) � � �� � � �� � � ϕ ( n ) 1 1 + f g f g d η ( g ) 0 n c (Γ) SL (2 , R ) n ∈ N (Γ) f ) k for all k ∈ N . (F. 2019) integral formula for ( �

  24. Theorem (Fairchild ’19) � � � 2 ( g ) d µ ( g ) � f SL (2 , R ) / Γ � 1 = R 2 f ( x ) f ( x ) + f ( x ) f ( − x ) dx c (Γ) � � �� � � �� � � ϕ ( n ) 1 1 + f g f g d η ( g ) 0 n c (Γ) SL (2 , R ) n ∈ N (Γ) N (Γ) is set of possible determinants. N (Γ) = { n ∈ R : ∃ v 1 , v 2 ∈ V s.t. | v 1 ∧ v 2 | = n } .

  25. Theorem (Fairchild ’19) � � � 2 ( g ) d µ ( g ) � f SL (2 , R ) / Γ � 1 = R 2 f ( x ) f ( x ) + f ( x ) f ( − x ) dx c (Γ) � � �� � � �� � � ϕ ( n ) 1 1 + f g f g d η ( g ) 0 c (Γ) n SL (2 , R ) n ∈ N (Γ) �� �� 1 h 1 Maximal parabolic Γ 0 = stab σ − 1 Γ σ ( e 1 ) = . 0 1

  26. Theorem (Fairchild ’19) � � � 2 ( g ) d µ ( g ) � f SL (2 , R ) / Γ � 1 = R 2 f ( x ) f ( x ) + f ( x ) f ( − x ) dx c (Γ) � � �� � � �� � � ϕ ( n ) 1 1 + f g f g d η ( g ) 0 c (Γ) n SL (2 , R ) n ∈ N (Γ) �� �� 1 h 1 Maximal parabolic Γ 0 = stab σ − 1 Γ σ ( e 1 ) = . 0 1 2 � �� � �� � � � � �� � � � � m ∗ ∗ � � � � ϕ ( n ) = ∈ V : 0 ≤ m < h | n | � = Γ 0 γ Γ 0 : γ = ∈ Γ � . � � � � � � ∗ n n

  27. Sketch of Proof Theorem (Fairchild ’19) � � � 2 ( g ) d µ ( g ) � f SL (2 , R ) / Γ � 1 = R 2 f ( x ) f ( x ) + f ( x ) f ( − x ) dx c (Γ) � � �� � � �� � � ϕ ( n ) 1 1 + f g f g d η ( g ) c (Γ) 0 n SL (2 , R ) n ∈ N (Γ) Note � f : SL (2 , R ) / Γ → R � � f ( g ) = f ( gv ) v ∈ V Implies � � 2 ( g ) = � � f f ( gv 1 ) f ( gv 2 ) ( v 1 , v 2 ) ∈ V × V

  28. Sketch of Proof Theorem (Fairchild ’19) � � � 2 ( g ) d µ ( g ) � f SL (2 , R ) / Γ � 1 = R 2 f ( x ) f ( x ) + f ( x ) f ( − x ) dx c (Γ) � � �� � � �� � � ϕ ( n ) 1 1 + f g f g d η ( g ) c (Γ) 0 n SL (2 , R ) n ∈ N (Γ) Decompose V × V into SL (2 , R )-orbits: V × V = { ( v , v ) : v ∈ V } ⊔ { ( v , − v ) : v ∈ V }⊔ � { ( v , w ) ∈ V × V : | v ∧ w | = n } n ∈ N (Γ)

  29. Theorem (Fairchild ’19) � � � 2 ( g ) d µ ( g ) � f SL (2 , R ) / Γ � 1 = R 2 f ( x ) f ( x ) + f ( x ) f ( − x ) dx c (Γ) � � �� � � �� � � ϕ ( n ) 1 1 + d η ( g ) f g f g 0 n c (Γ) SL (2 , R ) n ∈ N (Γ) Decompose V × V into SL (2 , R )-orbits: V × V = { ( v , v ) : v ∈ V } ⊔ { ( v , − v ) : v ∈ V }⊔ � { ( v , w ) ∈ V × V : | v ∧ w | = n } n ∈ N (Γ)

  30. Theorem (Fairchild ’19) � � � 2 ( g ) d µ ( g ) � f SL (2 , R ) / Γ � 1 = R 2 f ( x ) f ( x ) + f ( x ) f ( − x ) dx c (Γ) � � �� � � �� � � ϕ ( n ) 1 1 + d η ( g ) f g f g 0 n c (Γ) SL (2 , R ) n ∈ N (Γ) Decompose V × V into SL (2 , R )-orbits: V × V = { ( v , v ) : v ∈ V } ⊔ { ( v , − v ) : v ∈ V }⊔ � { ( v , w ) ∈ V × V : | v ∧ w | = n } n ∈ N (Γ)

  31. Reduction to Γ orbits of D n Lemma � � f ( gv 1 ) f ( gv 2 ) d µ ( g ) SL (2 , R ) / Γ ( v 1 , v 2 ) ∈ D n � � �� � � �� � = ϕ ( n ) 1 1 f g f g d η ( g ) 0 c (Γ) n SL (2 , R ) For n ∈ N (Γ) define D n = { ( v , w ) ∈ V × V : | v ∧ w | = n } Want to use � � � 1 f ( g γ v 1 ) f ( g γ v 2 ) d µ ( g ) = f ( gv 1 ) f ( gv 2 ) d η ( g ) . c (Γ) SL (2 , R ) / Γ SL (2 , R ) γ ∈ Γ

  32. ϕ is number of Γ orbits of D n Lemma � � � 1 j D n = Γ · 0 n 1 ≤ j ≤ h | n | ( j , n ) T ∈ V Thus there are ϕ ( n ) orbits. Each has a contribution of � � �� � � �� � 1 1 j d η ( g ) f g f g 0 c (Γ) n SL (2 , R )

  33. ϕ is number of Γ orbits of D n Lemma � � � 1 j D n = Γ · 0 n 1 ≤ j ≤ h | n | ( j , n ) T ∈ V Thus there are ϕ ( n ) orbits. Each has a contribution of � � �� � � �� � 1 1 j d η ( g ) f g f g 0 c (Γ) n SL (2 , R ) Theorem � � f ( gv 1 ) f ( gv 2 ) d µ ( g ) SL (2 , R ) / Γ v 1 , v 2 ∈ D n � � �� � � �� � = ϕ ( n ) 1 1 d η ( g ) f g f g 0 n c (Γ) SL (2 , R )

  34. From integrals to asymptotics � � �� � � �� � � ϕ ( n ) 1 1 d η f g f g 0 n c (Γ) SL (2 , R ) n ∈ N (Γ) � � 1 = R 2 f ( x ) f ( y ) ω ( | x ∧ y | ) dx dy c (Γ) R 2 where � ϕ ( n ) ω ( t ) = n 3 n ≥ t n ∈ N (Γ)

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend


More recommend