Diffraction Spectra of Weighted Delone Sets on Beta-Lattices with Beta a Quadratic Unitary Pisot Number Tome 56, No 7 (2006), P
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Arithmetic Equivalence and Isospectrality
ARITHMETIC EQUIVALENCE AND ISOSPECTRALITY ANDREW V.SUTHERLAND ABSTRACT. In these lecture notes we give an introduction to the theory of arithmetic equivalence, a notion originally introduced in a number theoretic setting to refer to number fields with the same zeta function. Gassmann established a direct relationship between arithmetic equivalence and a purely group theoretic notion of equivalence that has since been exploited in several other areas of mathematics, most notably in the spectral theory of Riemannian manifolds by Sunada. We will explicate these results and discuss some applications and generalizations. 1. AN INTRODUCTION TO ARITHMETIC EQUIVALENCE AND ISOSPECTRALITY Let K be a number field (a finite extension of Q), and let OK be its ring of integers (the integral closure of Z in K). The Dedekind zeta function of K is defined by the Dirichlet series X s Y s 1 ζK (s) := N(I)− = (1 N(p)− )− I OK p − ⊆ where the sum ranges over nonzero OK -ideals, the product ranges over nonzero prime ideals, and N(I) := [OK : I] is the absolute norm. For K = Q the Dedekind zeta function ζQ(s) is simply the : P s Riemann zeta function ζ(s) = n 1 n− . As with the Riemann zeta function, the Dirichlet series (and corresponding Euler product) defining≥ the Dedekind zeta function converges absolutely and uniformly to a nonzero holomorphic function on Re(s) > 1, and ζK (s) extends to a meromorphic function on C and satisfies a functional equation, as shown by Hecke [25]. The Dedekind zeta function encodes many features of the number field K: it has a simple pole at s = 1 whose residue is intimately related to several invariants of K, including its class number, and as with the Riemann zeta function, the zeros of ζK (s) are intimately related to the distribution of prime ideals in OK . -
NOTES on FINITE GROUP REPRESENTATIONS in Fall 2020, I
NOTES ON FINITE GROUP REPRESENTATIONS CHARLES REZK In Fall 2020, I taught an undergraduate course on abstract algebra. I chose to spend two weeks on the theory of complex representations of finite groups. I covered the basic concepts, leading to the classification of representations by characters. I also briefly addressed a few more advanced topics, notably induced representations and Frobenius divisibility. I'm making the lectures and these associated notes for this material publicly available. The material here is standard, and is mainly based on Steinberg, Representation theory of finite groups, Ch 2-4, whose notation I will mostly follow. I also used Serre, Linear representations of finite groups, Ch 1-3.1 1. Group representations Given a vector space V over a field F , we write GL(V ) for the group of bijective linear maps T : V ! V . n n When V = F we can write GLn(F ) = GL(F ), and identify the group with the group of invertible n × n matrices. A representation of a group G is a homomorphism of groups φ: G ! GL(V ) for some representation choice of vector space V . I'll usually write φg 2 GL(V ) for the value of φ on g 2 G. n When V = F , so we have a homomorphism φ: G ! GLn(F ), we call it a matrix representation. matrix representation The choice of field F matters. For now, we will look exclusively at the case of F = C, i.e., representations in complex vector spaces. Remark. Since R ⊆ C is a subfield, GLn(R) is a subgroup of GLn(C). -
Class Numbers of Totally Real Number Fields
CLASS NUMBERS OF TOTALLY REAL NUMBER FIELDS BY JOHN C. MILLER A dissertation submitted to the Graduate School|New Brunswick Rutgers, The State University of New Jersey in partial fulfillment of the requirements for the degree of Doctor of Philosophy Graduate Program in Mathematics Written under the direction of Henryk Iwaniec and approved by New Brunswick, New Jersey May, 2015 ABSTRACT OF THE DISSERTATION Class numbers of totally real number fields by John C. Miller Dissertation Director: Henryk Iwaniec The determination of the class number of totally real fields of large discriminant is known to be a difficult problem. The Minkowski bound is too large to be useful, and the root discriminant of the field can be too large to be treated by Odlyzko's discriminant bounds. This thesis describes a new approach. By finding nontrivial lower bounds for sums over prime ideals of the Hilbert class field, we establish upper bounds for class numbers of fields of larger discriminant. This analytic upper bound, together with algebraic arguments concerning the divisibility properties of class numbers, allows us to determine the class numbers of many number fields that have previously been untreatable by any known method. For example, we consider the cyclotomic fields and their real subfields. Surprisingly, the class numbers of cyclotomic fields have only been determined for fields of small conductor, e.g. for prime conductors up to 67, due to the problem of finding the class number of its maximal real subfield, a problem first considered by Kummer. Our results have significantly improved the situation. We also study the cyclotomic Zp-extensions of the rationals. -
REPRESENTATION THEORY WEEK 7 1. Characters of GL Kand Sn A
REPRESENTATION THEORY WEEK 7 1. Characters of GLk and Sn A character of an irreducible representation of GLk is a polynomial function con- stant on every conjugacy class. Since the set of diagonalizable matrices is dense in GLk, a character is defined by its values on the subgroup of diagonal matrices in GLk. Thus, one can consider a character as a polynomial function of x1,...,xk. Moreover, a character is a symmetric polynomial of x1,...,xk as the matrices diag (x1,...,xk) and diag xs(1),...,xs(k) are conjugate for any s ∈ Sk. For example, the character of the standard representation in E is equal to x1 + ⊗n n ··· + xk and the character of E is equal to (x1 + ··· + xk) . Let λ = (λ1,...,λk) be such that λ1 ≥ λ2 ≥ ···≥ λk ≥ 0. Let Dλ denote the λj determinant of the k × k-matrix whose i, j entry equals xi . It is clear that Dλ is a skew-symmetric polynomial of x1,...,xk. If ρ = (k − 1,..., 1, 0) then Dρ = i≤j (xi − xj) is the well known Vandermonde determinant. Let Q Dλ+ρ Sλ = . Dρ It is easy to see that Sλ is a symmetric polynomial of x1,...,xk. It is called a Schur λ1 λk polynomial. The leading monomial of Sλ is the x ...xk (if one orders monomials lexicographically) and therefore it is not hard to show that Sλ form a basis in the ring of symmetric polynomials of x1,...,xk. Theorem 1.1. The character of Wλ equals to Sλ. I do not include a proof of this Theorem since it uses beautiful but hard combina- toric. -
Problem 1 Show That If Π Is an Irreducible Representation of a Compact Lie Group G Then Π∨ Is Also Irreducible
Problem 1 Show that if π is an irreducible representation of a compact lie group G then π_ is also irreducible. Give an example of a G and π such that π =∼ π_, and another for which π π_. Is this true for general groups? R 2 Since G is a compact Lie group, we can apply Schur orthogonality to see that G jχπ_ (g)j dg = R 2 R 2 _ G jχπ(g)j dg = G jχπ(g)j dg = 1, so π is irreducible. For any G, the trivial representation π _ i i satisfies π ' π . For G the cyclic group of order 3 generated by g, the representation π : g 7! ζ3 _ i −i is not isomorphic to π : g 7! ζ3 . This is true for finite dimensional irreducible representations. This follows as if W ⊆ V a proper, non-zero G-invariant representation for a group G, then W ? = fφ 2 V ∗jφ(W ) = 0g is a proper, non-zero G-invariant subspace and applying this to V ∗ as V ∗∗ =∼ V we get V is irreducible if and only if V ∗ is irreducible. This is false for general representations and general groups. Take G = S1 to be the symmetric group on countably infinitely many letters, and let G act on a vector space V over C with basis e1; e2; ··· by sending σ : ei 7! eσ(i). Let W be the sub-representation given by the kernel of the map P P V ! C sending λiei 7! λi. Then W is irreducible because given any λ1e1 + ··· + λnen 2 V −1 where λn 6= 0, and we see that λn [(n n + 1) − 1] 2 C[G] sends this to en+1 − en, and elements like ∗ ∗ this span W . -
Extending Real-Valued Characters of Finite General Linear and Unitary Groups on Elements Related to Regular Unipotents
EXTENDING REAL-VALUED CHARACTERS OF FINITE GENERAL LINEAR AND UNITARY GROUPS ON ELEMENTS RELATED TO REGULAR UNIPOTENTS ROD GOW AND C. RYAN VINROOT Abstract. Let GL(n; Fq)hτi and U(n; Fq2 )hτi denote the finite general linear and unitary groups extended by the transpose inverse automorphism, respec- tively, where q is a power of p. Let n be odd, and let χ be an irreducible character of either of these groups which is an extension of a real-valued char- acter of GL(n; Fq) or U(n; Fq2 ). Let yτ be an element of GL(n; Fq)hτi or 2 U(n; Fq2 )hτi such that (yτ) is regular unipotent in GL(n; Fq) or U(n; Fq2 ), respectively. We show that χ(yτ) = ±1 if χ(1) is prime to p and χ(yτ) = 0 oth- erwise. Several intermediate results on real conjugacy classes and real-valued characters of these groups are obtained along the way. 1. Introduction Let F be a field and let n be a positive integer. Let GL(n; F) denote the general linear group of degree n over F. In the special case that F is the finite field of order q, we denote the corresponding general linear group by GL(n; Fq). Let τ denote the involutory automorphism of GL(n; F) which maps an element g to its transpose inverse (g0)−1, where g0 denotes the transpose of g, and let GL(n; F)hτi denote the semidirect product of GL(n; F) by τ. Thus in GL(n; F)hτi, we have τ 2 = 1 and τgτ = (g0)−1 for g 2 GL(n; F). -
Observational Astrophysics II January 18, 2007
Observational Astrophysics II JOHAN BLEEKER SRON Netherlands Institute for Space Research Astronomical Institute Utrecht FRANK VERBUNT Astronomical Institute Utrecht January 18, 2007 Contents 1RadiationFields 4 1.1 Stochastic processes: distribution functions, mean and variance . 4 1.2Autocorrelationandautocovariance........................ 5 1.3Wide-sensestationaryandergodicsignals..................... 6 1.4Powerspectraldensity................................ 7 1.5 Intrinsic stochastic nature of a radiation beam: Application of Bose-Einstein statistics ....................................... 10 1.5.1 Intermezzo: Bose-Einstein statistics . ................... 10 1.5.2 Intermezzo: Fermi Dirac statistics . ................... 13 1.6 Stochastic description of a radiation field in the thermal limit . 15 1.7 Stochastic description of a radiation field in the quantum limit . 20 2 Astronomical measuring process: information transfer 23 2.1Integralresponsefunctionforastronomicalmeasurements............ 23 2.2Timefiltering..................................... 24 2.2.1 Finiteexposureandtimeresolution.................... 24 2.2.2 Error assessment in sample average μT ................... 25 2.3 Data sampling in a bandlimited measuring system: Critical or Nyquist frequency 28 3 Indirect Imaging and Spectroscopy 31 3.1Coherence....................................... 31 3.1.1 The Visibility function . ................... 31 3.1.2 Young’sdualbeaminterferenceexperiment................ 31 3.1.3 Themutualcoherencefunction....................... 32 3.1.4 Interference -
Math 263A Notes: Algebraic Combinatorics and Symmetric Functions
MATH 263A NOTES: ALGEBRAIC COMBINATORICS AND SYMMETRIC FUNCTIONS AARON LANDESMAN CONTENTS 1. Introduction 4 2. 10/26/16 5 2.1. Logistics 5 2.2. Overview 5 2.3. Down to Math 5 2.4. Partitions 6 2.5. Partial Orders 7 2.6. Monomial Symmetric Functions 7 2.7. Elementary symmetric functions 8 2.8. Course Outline 8 3. 9/28/16 9 3.1. Elementary symmetric functions eλ 9 3.2. Homogeneous symmetric functions, hλ 10 3.3. Power sums pλ 12 4. 9/30/16 14 5. 10/3/16 20 5.1. Expected Number of Fixed Points 20 5.2. Random Matrix Groups 22 5.3. Schur Functions 23 6. 10/5/16 24 6.1. Review 24 6.2. Schur Basis 24 6.3. Hall Inner product 27 7. 10/7/16 29 7.1. Basic properties of the Cauchy product 29 7.2. Discussion of the Cauchy product and related formulas 30 8. 10/10/16 32 8.1. Finishing up last class 32 8.2. Skew-Schur Functions 33 8.3. Jacobi-Trudi 36 9. 10/12/16 37 1 2 AARON LANDESMAN 9.1. Eigenvalues of unitary matrices 37 9.2. Application 39 9.3. Strong Szego limit theorem 40 10. 10/14/16 41 10.1. Background on Tableau 43 10.2. KOSKA Numbers 44 11. 10/17/16 45 11.1. Relations of skew-Schur functions to other fields 45 11.2. Characters of the symmetric group 46 12. 10/19/16 49 13. 10/21/16 55 13.1. -
On the Duality of Regular and Local Functions
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 24 May 2017 doi:10.20944/preprints201705.0175.v1 mathematics ISSN 2227-7390 www.mdpi.com/journal/mathematics Article On the Duality of Regular and Local Functions Jens V. Fischer Institute of Mathematics, Ludwig Maximilians University Munich, 80333 Munich, Germany; E-Mail: jens.fi[email protected]; Tel.: +49-8196-934918 1 Abstract: In this paper, we relate Poisson’s summation formula to Heisenberg’s uncertainty 2 principle. They both express Fourier dualities within the space of tempered distributions and 3 these dualities are furthermore the inverses of one another. While Poisson’s summation 4 formula expresses a duality between discretization and periodization, Heisenberg’s 5 uncertainty principle expresses a duality between regularization and localization. We define 6 regularization and localization on generalized functions and show that the Fourier transform 7 of regular functions are local functions and, vice versa, the Fourier transform of local 8 functions are regular functions. 9 Keywords: generalized functions; tempered distributions; regular functions; local functions; 10 regularization-localization duality; regularity; Heisenberg’s uncertainty principle 11 Classification: MSC 42B05, 46F10, 46F12 1 1.2 Introduction 13 Regularization is a popular trick in applied mathematics, see [1] for example. It is the technique 14 ”to approximate functions by more differentiable ones” [2]. Its terminology coincides moreover with 15 the terminology used in generalized function spaces. They contain two kinds of functions, ”regular 16 functions” and ”generalized functions”. While regular functions are being functions in the ordinary 17 functions sense which are infinitely differentiable in the ordinary functions sense, all other functions 18 become ”infinitely differentiable” in the ”generalized functions sense” [3]. -
Algorithmic Framework for X-Ray Nanocrystallographic Reconstruction in the Presence of the Indexing Ambiguity
Algorithmic framework for X-ray nanocrystallographic reconstruction in the presence of the indexing ambiguity Jeffrey J. Donatellia,b and James A. Sethiana,b,1 aDepartment of Mathematics and bLawrence Berkeley National Laboratory, University of California, Berkeley, CA 94720 Contributed by James A. Sethian, November 21, 2013 (sent for review September 23, 2013) X-ray nanocrystallography allows the structure of a macromolecule over multiple orientations. Although there has been some success in to be determined from a large ensemble of nanocrystals. How- determining structure from perfectly twinned data, reconstruction is ever, several parameters, including crystal sizes, orientations, and often infeasible without a good initial atomic model of the structure. incident photon flux densities, are initially unknown and images We present an algorithmic framework for X-ray nanocrystal- are highly corrupted with noise. Autoindexing techniques, com- lographic reconstruction which is based on directly reducing data monly used in conventional crystallography, can determine ori- variance and resolving the indexing ambiguity. First, we design entations using Bragg peak patterns, but only up to crystal lattice an autoindexing technique that uses both Bragg and non-Bragg symmetry. This limitation results in an ambiguity in the orienta- data to compute precise orientations, up to lattice symmetry. tions, known as the indexing ambiguity, when the diffraction Crystal sizes are then determined by performing a Fourier analysis pattern displays less symmetry than the lattice and leads to data around Bragg peak neighborhoods from a finely sampled low- that appear twinned if left unresolved. Furthermore, missing angle image, such as from a rear detector (Fig. 1). Next, we model phase information must be recovered to determine the imaged structure factor magnitudes for each reciprocal lattice point with object’s structure. -
Understanding the Lomb-Scargle Periodogram
Understanding the Lomb-Scargle Periodogram Jacob T. VanderPlas1 ABSTRACT The Lomb-Scargle periodogram is a well-known algorithm for detecting and char- acterizing periodic signals in unevenly-sampled data. This paper presents a conceptual introduction to the Lomb-Scargle periodogram and important practical considerations for its use. Rather than a rigorous mathematical treatment, the goal of this paper is to build intuition about what assumptions are implicit in the use of the Lomb-Scargle periodogram and related estimators of periodicity, so as to motivate important practical considerations required in its proper application and interpretation. Subject headings: methods: data analysis | methods: statistical 1. Introduction The Lomb-Scargle periodogram (Lomb 1976; Scargle 1982) is a well-known algorithm for de- tecting and characterizing periodicity in unevenly-sampled time-series, and has seen particularly wide use within the astronomy community. As an example of a typical application of this method, consider the data shown in Figure 1: this is an irregularly-sampled timeseries showing a single ob- ject from the LINEAR survey (Sesar et al. 2011; Palaversa et al. 2013), with un-filtered magnitude measured 280 times over the course of five and a half years. By eye, it is clear that the brightness of the object varies in time with a range spanning approximately 0.8 magnitudes, but what is not immediately clear is that this variation is periodic in time. The Lomb-Scargle periodogram is a method that allows efficient computation of a Fourier-like power spectrum estimator from such unevenly-sampled data, resulting in an intuitive means of determining the period of oscillation. -
GROUP REPRESENTATIONS and CHARACTER THEORY Contents 1
GROUP REPRESENTATIONS AND CHARACTER THEORY DAVID KANG Abstract. In this paper, we provide an introduction to the representation theory of finite groups. We begin by defining representations, G-linear maps, and other essential concepts before moving quickly towards initial results on irreducibility and Schur's Lemma. We then consider characters, class func- tions, and show that the character of a representation uniquely determines it up to isomorphism. Orthogonality relations are introduced shortly afterwards. Finally, we construct the character tables for a few familiar groups. Contents 1. Introduction 1 2. Preliminaries 1 3. Group Representations 2 4. Maschke's Theorem and Complete Reducibility 4 5. Schur's Lemma and Decomposition 5 6. Character Theory 7 7. Character Tables for S4 and Z3 12 Acknowledgments 13 References 14 1. Introduction The primary motivation for the study of group representations is to simplify the study of groups. Representation theory offers a powerful approach to the study of groups because it reduces many group theoretic problems to basic linear algebra calculations. To this end, we assume that the reader is already quite familiar with linear algebra and has had some exposure to group theory. With this said, we begin with a preliminary section on group theory. 2. Preliminaries Definition 2.1. A group is a set G with a binary operation satisfying (1) 8 g; h; i 2 G; (gh)i = g(hi)(associativity) (2) 9 1 2 G such that 1g = g1 = g; 8g 2 G (identity) (3) 8 g 2 G; 9 g−1 such that gg−1 = g−1g = 1 (inverses) Definition 2.2.