Notes on Fourier Series and Modular Forms
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Topological Modes in Dual Lattice Models
Topological Modes in Dual Lattice Models Mark Rakowski 1 Dublin Institute for Advanced Studies, 10 Burlington Road, Dublin 4, Ireland Abstract Lattice gauge theory with gauge group ZP is reconsidered in four dimensions on a simplicial complex K. One finds that the dual theory, formulated on the dual block complex Kˆ , contains topological modes 2 which are in correspondence with the cohomology group H (K,Zˆ P ), in addition to the usual dynamical link variables. This is a general phenomenon in all models with single plaquette based actions; the action of the dual theory becomes twisted with a field representing the above cohomology class. A similar observation is made about the dual version of the three dimensional Ising model. The importance of distinct topological sectors is confirmed numerically in the two di- 1 mensional Ising model where they are parameterized by H (K,Zˆ 2). arXiv:hep-th/9410137v1 19 Oct 1994 PACS numbers: 11.15.Ha, 05.50.+q October 1994 DIAS Preprint 94-32 1Email: [email protected] 1 Introduction The use of duality transformations in statistical systems has a long history, beginning with applications to the two dimensional Ising model [1]. Here one finds that the high and low temperature properties of the theory are related. This transformation has been extended to many other discrete models and is particularly useful when the symmetries involved are abelian; see [2] for an extensive review. All these studies have been confined to hypercubic lattices, or other regular structures, and these have rather limited global topological features. Since lattice models are defined in a way which depends clearly on the connectivity of links or other regions, one expects some sort of topological effects generally. -
The Convolution Theorem
Module 2 : Signals in Frequency Domain Lecture 18 : The Convolution Theorem Objectives In this lecture you will learn the following We shall prove the most important theorem regarding the Fourier Transform- the Convolution Theorem We are going to learn about filters. Proof of 'the Convolution theorem for the Fourier Transform'. The Dual version of the Convolution Theorem Parseval's theorem The Convolution Theorem We shall in this lecture prove the most important theorem regarding the Fourier Transform- the Convolution Theorem. It is this theorem that links the Fourier Transform to LSI systems, and opens up a wide range of applications for the Fourier Transform. We shall inspire its importance with an application example. Modulation Modulation refers to the process of embedding an information-bearing signal into a second carrier signal. Extracting the information- bearing signal is called demodulation. Modulation allows us to transmit information signals efficiently. It also makes possible the simultaneous transmission of more than one signal with overlapping spectra over the same channel. That is why we can have so many channels being broadcast on radio at the same time which would have been impossible without modulation There are several ways in which modulation is done. One technique is amplitude modulation or AM in which the information signal is used to modulate the amplitude of the carrier signal. Another important technique is frequency modulation or FM, in which the information signal is used to vary the frequency of the carrier signal. Let us consider a very simple example of AM. Consider the signal x(t) which has the spectrum X(f) as shown : Why such a spectrum ? Because it's the simplest possible multi-valued function. -
Lecture 7: Fourier Series and Complex Power Series 1 Fourier
Math 1d Instructor: Padraic Bartlett Lecture 7: Fourier Series and Complex Power Series Week 7 Caltech 2013 1 Fourier Series 1.1 Definitions and Motivation Definition 1.1. A Fourier series is a series of functions of the form 1 C X + (a sin(nx) + b cos(nx)) ; 2 n n n=1 where C; an; bn are some collection of real numbers. The first, immediate use of Fourier series is the following theorem, which tells us that they (in a sense) can approximate far more functions than power series can: Theorem 1.2. Suppose that f(x) is a real-valued function such that • f(x) is continuous with continuous derivative, except for at most finitely many points in [−π; π]. • f(x) is periodic with period 2π: i.e. f(x) = f(x ± 2π), for any x 2 R. C P1 Then there is a Fourier series 2 + n=1 (an sin(nx) + bn cos(nx)) such that 1 C X f(x) = + (a sin(nx) + b cos(nx)) : 2 n n n=1 In other words, where power series can only converge to functions that are continu- ous and infinitely differentiable everywhere the power series is defined, Fourier series can converge to far more functions! This makes them, in practice, a quite useful concept, as in science we'll often want to study functions that aren't always continuous, or infinitely differentiable. A very specific application of Fourier series is to sound and music! Specifically, re- call/observe that a musical note with frequency f is caused by the propogation of the lon- gitudinal wave sin(2πft) through some medium. -
Fourier Series
Academic Press Encyclopedia of Physical Science and Technology Fourier Series James S. Walker Department of Mathematics University of Wisconsin–Eau Claire Eau Claire, WI 54702–4004 Phone: 715–836–3301 Fax: 715–836–2924 e-mail: [email protected] 1 2 Encyclopedia of Physical Science and Technology I. Introduction II. Historical background III. Definition of Fourier series IV. Convergence of Fourier series V. Convergence in norm VI. Summability of Fourier series VII. Generalized Fourier series VIII. Discrete Fourier series IX. Conclusion GLOSSARY ¢¤£¦¥¨§ Bounded variation: A function has bounded variation on a closed interval ¡ ¢ if there exists a positive constant © such that, for all finite sets of points "! "! $#&% (' #*) © ¥ , the inequality is satisfied. Jordan proved that a function has bounded variation if and only if it can be expressed as the difference of two non-decreasing functions. Countably infinite set: A set is countably infinite if it can be put into one-to-one £0/"£ correspondence with the set of natural numbers ( +,£¦-.£ ). Examples: The integers and the rational numbers are countably infinite sets. "! "!;: # # 123547698 Continuous function: If , then the function is continuous at the point : . Such a point is called a continuity point for . A function which is continuous at all points is simply referred to as continuous. Lebesgue measure zero: A set < of real numbers is said to have Lebesgue measure ! $#¨CED B ¢ £¦¥ zero if, for each =?>A@ , there exists a collection of open intervals such ! ! D D J# K% $#L) ¢ £¦¥ ¥ ¢ = that <GFIH and . Examples: All finite sets, and all countably infinite sets, have Lebesgue measure zero. "! "! % # % # Odd and even functions: A function is odd if for all in its "! "! % # # domain. -
The Summation of Power Series and Fourier Series
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Journal of Computational and Applied Mathematics 12&13 (1985) 447-457 447 North-Holland The summation of power series and Fourier . series I.M. LONGMAN Department of Geophysics and Planetary Sciences, Tel Aviv University, Ramat Aviv, Israel Received 27 July 1984 Abstract: The well-known correspondence of a power series with a certain Stieltjes integral is exploited for summation of the series by numerical integration. The emphasis in this paper is thus on actual summation of series. rather than mere acceleration of convergence. It is assumed that the coefficients of the series are given analytically, and then the numerator of the integrand is determined by the aid of the inverse of the two-sided Laplace transform, while the denominator is standard (and known) for all power series. Since Fourier series can be expressed in terms of power series, the method is applicable also to them. The treatment is extended to divergent series, and a fair number of numerical examples are given, in order to illustrate various techniques for the numerical evaluation of the resulting integrals. Keywork Summation of series. 1. Introduction We start by considering a power series, which it is convenient to write in the form s(x)=/.Q-~2x+&x2..., (1) for reasons which will become apparent presently. Here there is no intention to limit ourselves to alternating series since, even if the pLkare all of one sign, x may take negative and even complex values. It will be assumed in this paper that the pLk are real. -
Harmonic Analysis from Fourier to Wavelets
STUDENT MATHEMATICAL LIBRARY ⍀ IAS/PARK CITY MATHEMATICAL SUBSERIES Volume 63 Harmonic Analysis From Fourier to Wavelets María Cristina Pereyra Lesley A. Ward American Mathematical Society Institute for Advanced Study Harmonic Analysis From Fourier to Wavelets STUDENT MATHEMATICAL LIBRARY IAS/PARK CITY MATHEMATICAL SUBSERIES Volume 63 Harmonic Analysis From Fourier to Wavelets María Cristina Pereyra Lesley A. Ward American Mathematical Society, Providence, Rhode Island Institute for Advanced Study, Princeton, New Jersey Editorial Board of the Student Mathematical Library Gerald B. Folland Brad G. Osgood (Chair) Robin Forman John Stillwell Series Editor for the Park City Mathematics Institute John Polking 2010 Mathematics Subject Classification. Primary 42–01; Secondary 42–02, 42Axx, 42B25, 42C40. The anteater on the dedication page is by Miguel. The dragon at the back of the book is by Alexander. For additional information and updates on this book, visit www.ams.org/bookpages/stml-63 Library of Congress Cataloging-in-Publication Data Pereyra, Mar´ıa Cristina. Harmonic analysis : from Fourier to wavelets / Mar´ıa Cristina Pereyra, Lesley A. Ward. p. cm. — (Student mathematical library ; 63. IAS/Park City mathematical subseries) Includes bibliographical references and indexes. ISBN 978-0-8218-7566-7 (alk. paper) 1. Harmonic analysis—Textbooks. I. Ward, Lesley A., 1963– II. Title. QA403.P44 2012 515.2433—dc23 2012001283 Copying and reprinting. Individual readers of this publication, and nonprofit libraries acting for them, are permitted to make fair use of the material, such as to copy a chapter for use in teaching or research. Permission is granted to quote brief passages from this publication in reviews, provided the customary acknowledgment of the source is given. -
Convolution.Pdf
Convolution March 22, 2020 0.0.1 Setting up [23]: from bokeh.plotting import figure from bokeh.io import output_notebook, show,curdoc from bokeh.themes import Theme import numpy as np output_notebook() theme=Theme(json={}) curdoc().theme=theme 0.1 Convolution Convolution is a fundamental operation that appears in many different contexts in both pure and applied settings in mathematics. Polynomials: a simple case Let’s look at a simple case first. Consider the space of real valued polynomial functions P. If f 2 P, we have X1 i f(z) = aiz i=0 where ai = 0 for i sufficiently large. There is an obvious linear map a : P! c00(R) where c00(R) is the space of sequences that are eventually zero. i For each integer i, we have a projection map ai : c00(R) ! R so that ai(f) is the coefficient of z for f 2 P. Since P is a ring under addition and multiplication of functions, these operations must be reflected on the other side of the map a. 1 Clearly we can add sequences in c00(R) componentwise, and since addition of polynomials is also componentwise we have: a(f + g) = a(f) + a(g) What about multiplication? We know that X Xn X1 an(fg) = ar(f)as(g) = ar(f)an−r(g) = ar(f)an−r(g) r+s=n r=0 r=0 where we adopt the convention that ai(f) = 0 if i < 0. Given (a0; a1;:::) and (b0; b1;:::) in c00(R), define a ∗ b = (c0; c1;:::) where X1 cn = ajbn−j j=0 with the same convention that ai = bi = 0 if i < 0. -
The Sphere Packing Problem in Dimension 8 Arxiv:1603.04246V2
The sphere packing problem in dimension 8 Maryna S. Viazovska April 5, 2017 8 In this paper we prove that no packing of unit balls in Euclidean space R has density greater than that of the E8-lattice packing. Keywords: Sphere packing, Modular forms, Fourier analysis AMS subject classification: 52C17, 11F03, 11F30 1 Introduction The sphere packing constant measures which portion of d-dimensional Euclidean space d can be covered by non-overlapping unit balls. More precisely, let R be the Euclidean d vector space equipped with distance k · k and Lebesgue measure Vol(·). For x 2 R and d r 2 R>0 we denote by Bd(x; r) the open ball in R with center x and radius r. Let d X ⊂ R be a discrete set of points such that kx − yk ≥ 2 for any distinct x; y 2 X. Then the union [ P = Bd(x; 1) x2X d is a sphere packing. If X is a lattice in R then we say that P is a lattice sphere packing. The finite density of a packing P is defined as Vol(P\ Bd(0; r)) ∆P (r) := ; r > 0: Vol(Bd(0; r)) We define the density of a packing P as the limit superior ∆P := lim sup ∆P (r): r!1 arXiv:1603.04246v2 [math.NT] 4 Apr 2017 The number be want to know is the supremum over all possible packing densities ∆d := sup ∆P ; d P⊂R sphere packing 1 called the sphere packing constant. For which dimensions do we know the exact value of ∆d? Trivially, in dimension 1 we have ∆1 = 1. -
Sphere Packing, Lattice Packing, and Related Problems
Sphere packing, lattice packing, and related problems Abhinav Kumar Stony Brook April 25, 2018 Sphere packings Definition n A sphere packing in R is a collection of spheres/balls of equal size which do not overlap (except for touching). The density of a sphere packing is the volume fraction of space occupied by the balls. ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ In dimension 1, we can achieve density 1 by laying intervals end to end. In dimension 2, the best possible is by using the hexagonal lattice. [Fejes T´oth1940] Sphere packing problem n Problem: Find a/the densest sphere packing(s) in R . In dimension 2, the best possible is by using the hexagonal lattice. [Fejes T´oth1940] Sphere packing problem n Problem: Find a/the densest sphere packing(s) in R . In dimension 1, we can achieve density 1 by laying intervals end to end. Sphere packing problem n Problem: Find a/the densest sphere packing(s) in R . In dimension 1, we can achieve density 1 by laying intervals end to end. In dimension 2, the best possible is by using the hexagonal lattice. [Fejes T´oth1940] Sphere packing problem II In dimension 3, the best possible way is to stack layers of the solution in 2 dimensions. This is Kepler's conjecture, now a theorem of Hales and collaborators. mmm m mmm m There are infinitely (in fact, uncountably) many ways of doing this! These are the Barlow packings. Face centered cubic packing Image: Greg A L (Wikipedia), CC BY-SA 3.0 license But (until very recently!) no proofs. In very high dimensions (say ≥ 1000) densest packings are likely to be close to disordered. -
Physical Interpretation of the 30 8-Simplexes in the E8 240-Polytope
Physical Interpretation of the 30 8-simplexes in the E8 240-Polytope: Frank Dodd (Tony) Smith, Jr. 2017 - viXra 1702.0058 248-dim Lie Group E8 has 240 Root Vectors arranged on a 7-sphere S7 in 8-dim space. The 12 vertices of a cuboctahedron live on a 2-sphere S2 in 3-dim space. They are also the 4x3 = 12 outer vertices of 4 tetrahedra (3-simplexes) that share one inner vertex at the center of the cuboctahedron. This paper explores how the 240 vertices of the E8 Polytope in 8-dim space are related to the 30x8 = 240 outer vertices (red in figure below) of 30 8-simplexes whose 9th vertex is a shared inner vertex (yellow in figure below) at the center of the E8 Polytope. The 8-simplex has 9 vertices, 36 edges, 84 triangles, 126 tetrahedron cells, 126 4-simplex faces, 84 5-simplex faces, 36 6-simplex faces, 9 7-simplex faces, and 1 8-dim volume The real 4_21 Witting polytope of the E8 lattice in R8 has 240 vertices; 6,720 edges; 60,480 triangular faces; 241,920 tetrahedra; 483,840 4-simplexes; 483,840 5-simplexes 4_00; 138,240 + 69,120 6-simplexes 4_10 and 4_01; and 17,280 = 2,160x8 7-simplexes 4_20 and 2,160 7-cross-polytopes 4_11. The cuboctahedron corresponds by Jitterbug Transformation to the icosahedron. The 20 2-dim faces of an icosahedon in 3-dim space (image from spacesymmetrystructure.wordpress.com) are also the 20 outer faces of 20 not-exactly-regular-in-3-dim tetrahedra (3-simplexes) that share one inner vertex at the center of the icosahedron, but that correspondence does not extend to the case of 8-simplexes in an E8 polytope, whose faces are both 7-simplexes and 7-cross-polytopes, similar to the cubocahedron, but not its Jitterbug-transform icosahedron with only triangle = 2-simplex faces. -
Arxiv:2010.10200V3 [Math.GT] 1 Mar 2021 We Deduce in Particular the Following
HYPERBOLIC MANIFOLDS THAT FIBER ALGEBRAICALLY UP TO DIMENSION 8 GIOVANNI ITALIANO, BRUNO MARTELLI, AND MATTEO MIGLIORINI Abstract. We construct some cusped finite-volume hyperbolic n-manifolds Mn that fiber algebraically in all the dimensions 5 ≤ n ≤ 8. That is, there is a surjective homomorphism π1(Mn) ! Z with finitely generated kernel. The kernel is also finitely presented in the dimensions n = 7; 8, and this leads to the first examples of hyperbolic n-manifolds Mfn whose fundamental group is finitely presented but not of finite type. These n-manifolds Mfn have infinitely many cusps of maximal rank and hence infinite Betti number bn−1. They cover the finite-volume manifold Mn. We obtain these examples by assigning some appropriate colours and states to a family of right-angled hyperbolic polytopes P5;:::;P8, and then applying some arguments of Jankiewicz { Norin { Wise [15] and Bestvina { Brady [6]. We exploit in an essential way the remarkable properties of the Gosset polytopes dual to Pn, and the algebra of integral octonions for the crucial dimensions n = 7; 8. Introduction We prove here the following theorem. Every hyperbolic manifold in this paper is tacitly assumed to be connected, complete, and orientable. Theorem 1. In every dimension 5 ≤ n ≤ 8 there are a finite volume hyperbolic 1 n-manifold Mn and a map f : Mn ! S such that f∗ : π1(Mn) ! Z is surjective n with finitely generated kernel. The cover Mfn = H =ker f∗ has infinitely many cusps of maximal rank. When n = 7; 8 the kernel is also finitely presented. arXiv:2010.10200v3 [math.GT] 1 Mar 2021 We deduce in particular the following. -
Optimality and Uniqueness of the Leech Lattice Among Lattices
ANNALS OF MATHEMATICS Optimality and uniqueness of the Leech lattice among lattices By Henry Cohn and Abhinav Kumar SECOND SERIES, VOL. 170, NO. 3 November, 2009 anmaah Annals of Mathematics, 170 (2009), 1003–1050 Optimality and uniqueness of the Leech lattice among lattices By HENRY COHN and ABHINAV KUMAR Dedicated to Oded Schramm (10 December 1961 – 1 September 2008) Abstract We prove that the Leech lattice is the unique densest lattice in R24. The proof combines human reasoning with computer verification of the properties of certain explicit polynomials. We furthermore prove that no sphere packing in R24 can 30 exceed the Leech lattice’s density by a factor of more than 1 1:65 10 , and we 8 C give a new proof that E8 is the unique densest lattice in R . 1. Introduction It is a long-standing open problem in geometry and number theory to find the densest lattice in Rn. Recall that a lattice ƒ Rn is a discrete subgroup of rank n; a minimal vector in ƒ is a nonzero vector of minimal length. Let ƒ vol.Rn=ƒ/ j j D denote the covolume of ƒ, i.e., the volume of a fundamental parallelotope or the absolute value of the determinant of a basis of ƒ. If r is the minimal vector length of ƒ, then spheres of radius r=2 centered at the points of ƒ do not overlap except tangentially. This construction yields a sphere packing of density n=2 r Án 1 ; .n=2/Š 2 ƒ j j since the volume of a unit ball in Rn is n=2=.n=2/Š, where for odd n we define .n=2/Š .n=2 1/.