Solving the Geodesic Equation
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A Mathematical Derivation of the General Relativistic Schwarzschild
A Mathematical Derivation of the General Relativistic Schwarzschild Metric An Honors thesis presented to the faculty of the Departments of Physics and Mathematics East Tennessee State University In partial fulfillment of the requirements for the Honors Scholar and Honors-in-Discipline Programs for a Bachelor of Science in Physics and Mathematics by David Simpson April 2007 Robert Gardner, Ph.D. Mark Giroux, Ph.D. Keywords: differential geometry, general relativity, Schwarzschild metric, black holes ABSTRACT The Mathematical Derivation of the General Relativistic Schwarzschild Metric by David Simpson We briefly discuss some underlying principles of special and general relativity with the focus on a more geometric interpretation. We outline Einstein’s Equations which describes the geometry of spacetime due to the influence of mass, and from there derive the Schwarzschild metric. The metric relies on the curvature of spacetime to provide a means of measuring invariant spacetime intervals around an isolated, static, and spherically symmetric mass M, which could represent a star or a black hole. In the derivation, we suggest a concise mathematical line of reasoning to evaluate the large number of cumbersome equations involved which was not found elsewhere in our survey of the literature. 2 CONTENTS ABSTRACT ................................. 2 1 Introduction to Relativity ...................... 4 1.1 Minkowski Space ....................... 6 1.2 What is a black hole? ..................... 11 1.3 Geodesics and Christoffel Symbols ............. 14 2 Einstein’s Field Equations and Requirements for a Solution .17 2.1 Einstein’s Field Equations .................. 20 3 Derivation of the Schwarzschild Metric .............. 21 3.1 Evaluation of the Christoffel Symbols .......... 25 3.2 Ricci Tensor Components ................. -
Laplacians in Geometric Analysis
LAPLACIANS IN GEOMETRIC ANALYSIS Syafiq Johar syafi[email protected] Contents 1 Trace Laplacian 1 1.1 Connections on Vector Bundles . .1 1.2 Local and Explicit Expressions . .2 1.3 Second Covariant Derivative . .3 1.4 Curvatures on Vector Bundles . .4 1.5 Trace Laplacian . .5 2 Harmonic Functions 6 2.1 Gradient and Divergence Operators . .7 2.2 Laplace-Beltrami Operator . .7 2.3 Harmonic Functions . .8 2.4 Harmonic Maps . .8 3 Hodge Laplacian 9 3.1 Exterior Derivatives . .9 3.2 Hodge Duals . 10 3.3 Hodge Laplacian . 12 4 Hodge Decomposition 13 4.1 De Rham Cohomology . 13 4.2 Hodge Decomposition Theorem . 14 5 Weitzenb¨ock and B¨ochner Formulas 15 5.1 Weitzenb¨ock Formula . 15 5.1.1 0-forms . 15 5.1.2 k-forms . 15 5.2 B¨ochner Formula . 17 1 Trace Laplacian In this section, we are going to present a notion of Laplacian that is regularly used in differential geometry, namely the trace Laplacian (also called the rough Laplacian or connection Laplacian). We recall the definition of connection on vector bundles which allows us to take the directional derivative of vector bundles. 1.1 Connections on Vector Bundles Definition 1.1 (Connection). Let M be a differentiable manifold and E a vector bundle over M. A connection or covariant derivative at a point p 2 M is a map D : Γ(E) ! Γ(T ∗M ⊗ E) 1 with the properties for any V; W 2 TpM; σ; τ 2 Γ(E) and f 2 C (M), we have that DV σ 2 Ep with the following properties: 1. -
Vectors, Matrices and Coordinate Transformations
S. Widnall 16.07 Dynamics Fall 2009 Lecture notes based on J. Peraire Version 2.0 Lecture L3 - Vectors, Matrices and Coordinate Transformations By using vectors and defining appropriate operations between them, physical laws can often be written in a simple form. Since we will making extensive use of vectors in Dynamics, we will summarize some of their important properties. Vectors For our purposes we will think of a vector as a mathematical representation of a physical entity which has both magnitude and direction in a 3D space. Examples of physical vectors are forces, moments, and velocities. Geometrically, a vector can be represented as arrows. The length of the arrow represents its magnitude. Unless indicated otherwise, we shall assume that parallel translation does not change a vector, and we shall call the vectors satisfying this property, free vectors. Thus, two vectors are equal if and only if they are parallel, point in the same direction, and have equal length. Vectors are usually typed in boldface and scalar quantities appear in lightface italic type, e.g. the vector quantity A has magnitude, or modulus, A = |A|. In handwritten text, vectors are often expressed using the −→ arrow, or underbar notation, e.g. A , A. Vector Algebra Here, we introduce a few useful operations which are defined for free vectors. Multiplication by a scalar If we multiply a vector A by a scalar α, the result is a vector B = αA, which has magnitude B = |α|A. The vector B, is parallel to A and points in the same direction if α > 0. -
Kähler Manifolds, Ricci Curvature, and Hyperkähler Metrics
K¨ahlermanifolds, Ricci curvature, and hyperk¨ahler metrics Jeff A. Viaclovsky June 25-29, 2018 Contents 1 Lecture 1 3 1.1 The operators @ and @ ..........................4 1.2 Hermitian and K¨ahlermetrics . .5 2 Lecture 2 7 2.1 Complex tensor notation . .7 2.2 The musical isomorphisms . .8 2.3 Trace . 10 2.4 Determinant . 11 3 Lecture 3 11 3.1 Christoffel symbols of a K¨ahler metric . 11 3.2 Curvature of a Riemannian metric . 12 3.3 Curvature of a K¨ahlermetric . 14 3.4 The Ricci form . 15 4 Lecture 4 17 4.1 Line bundles and divisors . 17 4.2 Hermitian metrics on line bundles . 18 5 Lecture 5 21 5.1 Positivity of a line bundle . 21 5.2 The Laplacian on a K¨ahlermanifold . 22 5.3 Vanishing theorems . 25 6 Lecture 6 25 6.1 K¨ahlerclass and @@-Lemma . 25 6.2 Yau's Theorem . 27 6.3 The @ operator on holomorphic vector bundles . 29 1 7 Lecture 7 30 7.1 Holomorphic vector fields . 30 7.2 Serre duality . 32 8 Lecture 8 34 8.1 Kodaira vanishing theorem . 34 8.2 Complex projective space . 36 8.3 Line bundles on complex projective space . 37 8.4 Adjunction formula . 38 8.5 del Pezzo surfaces . 38 9 Lecture 9 40 9.1 Hirzebruch Signature Theorem . 40 9.2 Representations of U(2) . 42 9.3 Examples . 44 2 1 Lecture 1 We will assume a basic familiarity with complex manifolds, and only do a brief review today. Let M be a manifold of real dimension 2n, and an endomorphism J : TM ! TM satisfying J 2 = −Id. -
HARMONIC MAPS Contents 1. Introduction 2 1.1. Notational
HARMONIC MAPS ANDREW SANDERS Contents 1. Introduction 2 1.1. Notational conventions 2 2. Calculus on vector bundles 2 3. Basic properties of harmonic maps 7 3.1. First variation formula 7 References 10 1 2 ANDREW SANDERS 1. Introduction 1.1. Notational conventions. By a smooth manifold M we mean a second- countable Hausdorff topological space with a smooth maximal atlas. We denote the tangent bundle of M by TM and the cotangent bundle of M by T ∗M: 2. Calculus on vector bundles Given a pair of manifolds M; N and a smooth map f : M ! N; it is advantageous to consider the differential df : TM ! TN as a section df 2 Ω0(M; T ∗M ⊗ f ∗TN) ' Ω1(M; f ∗TN): There is a general for- malism for studying the calculus of differential forms with values in vector bundles equipped with a connection. This formalism allows a fairly efficient, and more coordinate-free, treatment of many calculations in the theory of harmonic maps. While this approach is somewhat abstract and obfuscates the analytic content of many expressions, it takes full advantage of the algebraic symmetries available and therefore simplifies many expressions. We will develop some of this theory now and use it freely throughout the text. The following exposition will closely fol- low [Xin96]. Let M be a smooth manifold and π : E ! M a real vector bundle on M or rank r: Throughout, we denote the space of smooth sections of E by Ω0(M; E): More generally, the space of differential p-forms with values in E is given by Ωp(M; E) := Ω0(M; ΛpT ∗M ⊗ E): Definition 2.1. -
General Relativity Fall 2019 Lecture 11: the Riemann Tensor
General Relativity Fall 2019 Lecture 11: The Riemann tensor Yacine Ali-Ha¨ımoud October 8th 2019 The Riemann tensor quantifies the curvature of spacetime, as we will see in this lecture and the next. RIEMANN TENSOR: BASIC PROPERTIES α γ Definition { Given any vector field V , r[αrβ]V is a tensor field. Let us compute its components in some coordinate system: σ σ λ σ σ λ r[µrν]V = @[µ(rν]V ) − Γ[µν]rλV + Γλ[µrν]V σ σ λ σ λ λ ρ = @[µ(@ν]V + Γν]λV ) + Γλ[µ @ν]V + Γν]ρV 1 = @ Γσ + Γσ Γρ V λ ≡ Rσ V λ; (1) [µ ν]λ ρ[µ ν]λ 2 λµν where all partial derivatives of V µ cancel out after antisymmetrization. σ Since the left-hand side is a tensor field and V is a vector field, we conclude that R λµν is a tensor field as well { this is the tensor division theorem, which I encourage you to think about on your own. You can also check that explicitly from the transformation law of Christoffel symbols. This is the Riemann tensor, which measures the non-commutation of second derivatives of vector fields { remember that second derivatives of scalar fields do commute, by assumption. It is completely determined by the metric, and is linear in its second derivatives. Expression in LICS { In a LICS the Christoffel symbols vanish but not their derivatives. Let us compute the latter: 1 1 @ Γσ = @ gσδ (@ g + @ g − @ g ) = ησδ (@ @ g + @ @ g − @ @ g ) ; (2) µ νλ 2 µ ν λδ λ νδ δ νλ 2 µ ν λδ µ λ νδ µ δ νλ since the first derivatives of the metric components (thus of its inverse as well) vanish in a LICS. -
Chapter 5 ANGULAR MOMENTUM and ROTATIONS
Chapter 5 ANGULAR MOMENTUM AND ROTATIONS In classical mechanics the total angular momentum L~ of an isolated system about any …xed point is conserved. The existence of a conserved vector L~ associated with such a system is itself a consequence of the fact that the associated Hamiltonian (or Lagrangian) is invariant under rotations, i.e., if the coordinates and momenta of the entire system are rotated “rigidly” about some point, the energy of the system is unchanged and, more importantly, is the same function of the dynamical variables as it was before the rotation. Such a circumstance would not apply, e.g., to a system lying in an externally imposed gravitational …eld pointing in some speci…c direction. Thus, the invariance of an isolated system under rotations ultimately arises from the fact that, in the absence of external …elds of this sort, space is isotropic; it behaves the same way in all directions. Not surprisingly, therefore, in quantum mechanics the individual Cartesian com- ponents Li of the total angular momentum operator L~ of an isolated system are also constants of the motion. The di¤erent components of L~ are not, however, compatible quantum observables. Indeed, as we will see the operators representing the components of angular momentum along di¤erent directions do not generally commute with one an- other. Thus, the vector operator L~ is not, strictly speaking, an observable, since it does not have a complete basis of eigenstates (which would have to be simultaneous eigenstates of all of its non-commuting components). This lack of commutivity often seems, at …rst encounter, as somewhat of a nuisance but, in fact, it intimately re‡ects the underlying structure of the three dimensional space in which we are immersed, and has its source in the fact that rotations in three dimensions about di¤erent axes do not commute with one another. -
Geodetic Position Computations
GEODETIC POSITION COMPUTATIONS E. J. KRAKIWSKY D. B. THOMSON February 1974 TECHNICALLECTURE NOTES REPORT NO.NO. 21739 PREFACE In order to make our extensive series of lecture notes more readily available, we have scanned the old master copies and produced electronic versions in Portable Document Format. The quality of the images varies depending on the quality of the originals. The images have not been converted to searchable text. GEODETIC POSITION COMPUTATIONS E.J. Krakiwsky D.B. Thomson Department of Geodesy and Geomatics Engineering University of New Brunswick P.O. Box 4400 Fredericton. N .B. Canada E3B5A3 February 197 4 Latest Reprinting December 1995 PREFACE The purpose of these notes is to give the theory and use of some methods of computing the geodetic positions of points on a reference ellipsoid and on the terrain. Justification for the first three sections o{ these lecture notes, which are concerned with the classical problem of "cCDputation of geodetic positions on the surface of an ellipsoid" is not easy to come by. It can onl.y be stated that the attempt has been to produce a self contained package , cont8.i.ning the complete development of same representative methods that exist in the literature. The last section is an introduction to three dimensional computation methods , and is offered as an alternative to the classical approach. Several problems, and their respective solutions, are presented. The approach t~en herein is to perform complete derivations, thus stqing awrq f'rcm the practice of giving a list of for11111lae to use in the solution of' a problem. -
Rotation Matrix - Wikipedia, the Free Encyclopedia Page 1 of 22
Rotation matrix - Wikipedia, the free encyclopedia Page 1 of 22 Rotation matrix From Wikipedia, the free encyclopedia In linear algebra, a rotation matrix is a matrix that is used to perform a rotation in Euclidean space. For example the matrix rotates points in the xy -Cartesian plane counterclockwise through an angle θ about the origin of the Cartesian coordinate system. To perform the rotation, the position of each point must be represented by a column vector v, containing the coordinates of the point. A rotated vector is obtained by using the matrix multiplication Rv (see below for details). In two and three dimensions, rotation matrices are among the simplest algebraic descriptions of rotations, and are used extensively for computations in geometry, physics, and computer graphics. Though most applications involve rotations in two or three dimensions, rotation matrices can be defined for n-dimensional space. Rotation matrices are always square, with real entries. Algebraically, a rotation matrix in n-dimensions is a n × n special orthogonal matrix, i.e. an orthogonal matrix whose determinant is 1: . The set of all rotation matrices forms a group, known as the rotation group or the special orthogonal group. It is a subset of the orthogonal group, which includes reflections and consists of all orthogonal matrices with determinant 1 or -1, and of the special linear group, which includes all volume-preserving transformations and consists of matrices with determinant 1. Contents 1 Rotations in two dimensions 1.1 Non-standard orientation -
Coordinate Transformation
Coordinate Transformation Coordinate Transformations In this chapter, we explore mappings – where a mapping is a function that "maps" one set to another, usually in a way that preserves at least some of the underlyign geometry of the sets. For example, a 2-dimensional coordinate transformation is a mapping of the form T (u; v) = x (u; v) ; y (u; v) h i The functions x (u; v) and y (u; v) are called the components of the transforma- tion. Moreover, the transformation T maps a set S in the uv-plane to a set T (S) in the xy-plane: If S is a region, then we use the components x = f (u; v) and y = g (u; v) to …nd the image of S under T (u; v) : EXAMPLE 1 Find T (S) when T (u; v) = uv; u2 v2 and S is the unit square in the uv-plane (i.e., S = [0; 1] [0; 1]). Solution: To do so, let’s determine the boundary of T (S) in the xy-plane. We use x = uv and y = u2 v2 to …nd the image of the lines bounding the unit square: Side of Square Result of T (u; v) Image in xy-plane v = 0; u in [0; 1] x = 0; y = u2; u in [0; 1] y-axis for 0 y 1 u = 1; v in [0; 1] x = v; y = 1 v2; v in [0; 1] y = 1 x2; x in[0; 1] v = 1; u in [0; 1] x = u; y = u2 1; u in [0; 1] y = x2 1; x in [0; 1] u = 0; u in [0; 1] x = 0; y = v2; v in [0; 1] y-axis for 1 y 0 1 As a result, T (S) is the region in the xy-plane bounded by x = 0; y = x2 1; and y = 1 x2: Linear transformations are coordinate transformations of the form T (u; v) = au + bv; cu + dv h i where a; b; c; and d are constants. -
3. Introducing Riemannian Geometry
3. Introducing Riemannian Geometry We have yet to meet the star of the show. There is one object that we can place on a manifold whose importance dwarfs all others, at least when it comes to understanding gravity. This is the metric. The existence of a metric brings a whole host of new concepts to the table which, collectively, are called Riemannian geometry.Infact,strictlyspeakingwewillneeda slightly di↵erent kind of metric for our study of gravity, one which, like the Minkowski metric, has some strange minus signs. This is referred to as Lorentzian Geometry and a slightly better name for this section would be “Introducing Riemannian and Lorentzian Geometry”. However, for our immediate purposes the di↵erences are minor. The novelties of Lorentzian geometry will become more pronounced later in the course when we explore some of the physical consequences such as horizons. 3.1 The Metric In Section 1, we informally introduced the metric as a way to measure distances between points. It does, indeed, provide this service but it is not its initial purpose. Instead, the metric is an inner product on each vector space Tp(M). Definition:Ametric g is a (0, 2) tensor field that is: Symmetric: g(X, Y )=g(Y,X). • Non-Degenerate: If, for any p M, g(X, Y ) =0forallY T (M)thenX =0. • 2 p 2 p p With a choice of coordinates, we can write the metric as g = g (x) dxµ dx⌫ µ⌫ ⌦ The object g is often written as a line element ds2 and this expression is abbreviated as 2 µ ⌫ ds = gµ⌫(x) dx dx This is the form that we saw previously in (1.4). -
Unit 5: Change of Coordinates
LINEAR ALGEBRA AND VECTOR ANALYSIS MATH 22B Unit 5: Change of Coordinates Lecture 5.1. Given a basis B in a linear space X, we can write an element v in X in a unique 3 way as a sum of basis elements. For example, if v = is a vector in X = 2 and 4 R 1 1 2 B = fv = ; v = g, then v = 2v + v . We say that are the B 1 −1 2 6 1 2 1 B 3 coordinates of v. The standard coordinates are v = are assumed if no other 4 basis is specified. This means v = 3e1 + 4e2. n 5.2. If B = fv1; v2; ··· ; vng is a basis of R , then the matrix S which contains the vectors vk as column vectors is called the coordinate change matrix. Theorem: If S is the matrix of B, then S−1v are the B coordinates of v. 1 1 6 −1 5.3. In the above example, S = has the inverse S−1 = =7. We −1 6 1 1 compute S−1[3; 4]T = [2; 1]T . Proof. If [v]B = [a1; : : : ; an] are the new coordinates of v, this means v = a1v1 + ··· + −1 anvn. But that means v = S[v]B. Since B is a basis, S is invertible and [v]B = S v. Theorem: If T (x) = Ax is a linear map and S is the matrix from a basis change, then B = S−1AS is the matrix of T in the new basis B. Proof. Let y = Ax. The statement [y]B = B[x]B can be written using the last theorem as S−1y = BS−1x so that y = SBS−1x.