(Dated: September 3, 2015)

Size: px
Start display at page:

Download "(Dated: September 3, 2015)"

Transcription

1 DOPPLER TRANSFORMATION AS EIGENVALUES OF LORENTZ TRANSFORMATION Louai Hassan Elzein Basheir 1 Physics College, Khartoum University, Sudan. P.O. Box: Zip Code: (Dated: September 3, 215) This paper has been prepared to show the derivation of the Doppler transformations as eigenvalues of the Lorentz transformations matrices and to conclusively proves that these eigenvalues are the correct relativistic factors which have to be use instead of the Lorentz factor. The paper also shows the forms of the Doppler matrices that can replace the Lorentz matrices, and discuss some of the consequences that follow the equivalence of Lorentz and Doppler transformations. 1

2 THEORETICAL BACKGROUND 1.1 Boost in the x-direction The Lorentz transformation for frames in standard configuration can be shown to be: ( t = γ t vx ) c 2 x = γ (x vt) (1) y = y z = z where: v is the relative velocity between frames in the x-direction, c is the speed of light, γ = 1 β 1 is the Lorentz factor, 2 β = v is the velocity coefficient, again for the x-direction. c Since the above is a linear system of equations, they can be written in matrix form: x γ iβγ x y z = 1 y 1 z (2) iβγ γ The above collection of equations apply only for a boost in the x-direction. The standard configuration works equally well in the y or z directions instead of x, and so the results are similar. For the y-direction: x 1 y z = γ iβγ 1 iβγ γ For the z-direction one obtains x 1 y z = 1 γ iβγ iβγ γ x y z x y z (3) (4) The Lorentz or boost matrix is usually denoted by Λ. Above the transformations have been applied to the four-position X, x x X = y z X = y z Page 2 of 14

3 The Lorentz transform for a boost in one of the above directions can be compactly written as a single matrix equation: X = Λ(v)X. (5) ANALYSIS 2.1 Eigenvalues of The Lorentz Matrix The Lorentz matrix has many properties, it is orthogonal where Λ 1 = Λ T, orthonormal where the magnitude of any of its unit column vector is equal to one (from this property one can obtain the value of the Lorentz factor γ), also it is symmetric where Λ = Λ T (for the Lorentz matrix with real elements) and it is hermitian (imaginary matrix). Since the Lorentz matrix has these properties, we expect to be able to derive eigenvalues from it, or simply to be able to write it in the following form: Λ(v)X = λx. (6) Equation (6) can be written as [Λ(v) λi] X = which implies that Λ(v) λi = and by solving this determinant one finds For the Lorentz matrix for x-direction: γ λ iβγ 1 λ 1 λ =, (7) iβγ γ λ and (γ λ) 1 λ 1 λ γ λ (iβγ) 1 λ 1 λ iβγ =, (γ λ) 2 (1 λ) 2 iβγ [ (1 λ) [ (1 λ)( iβγ)]] =, (γ λ) 2 (1 λ) 2 iβγ [ (1 λ) 2 ( iβγ) ] =, (γ λ) 2 (1 λ) 2 (1 λ) 2 (βγ) 2 =, (1 λ) [ 2 (γ λ) 2 (βγ) 2] =, which can factored: (1 λ) (1 λ) [(γ λ) + (βγ)] [(γ λ) (βγ)] =, (1 λ) (1 λ) [(γ + βγ) λ] [(γ βγ) λ] =, Page 3 of 14

4 which leads to the solutions: (γ βγ) λ = 1 λ = 1 λ = (γ + βγ) λ =, Thus the eigenvalues of the Lorentz matrix for x-direction: λ 1 = γ(1 β) λ 2 = 1 λ 3 = 1 λ 4 = γ(1 + β) (8) For y-direction and z-direction we obtain similar eigenvalues. Notice that λ 2 = λ 3 = 1 when the relative velocity between inertial frames is equal to zero, that is v = β =, γ = Eigenvectors of The Lorentz Matrix We know that the eigenvectors associated with eigenvalues have to be linearly independent and orthogonal, which implies its determinant has to be not equal to zero, so finding the eigenvectors matrix and exam its linear independency will check the validity of the derived eigenvalues (Eq.(8)). Substitute λ 1 into Eq.(7) yields the eigenvector associated with λ 1 for x-direction: γ γ(1 β) iβγ 1 γ(1 β) 1 γ(1 β) iβγ γ γ(1 β) βγ iβγ 1 γ(1 β) 1 γ(1 β) iβγ βγ (i(r 1 )) + R 4 R 4 βγ iβγ 1 γ(1 β) 1 γ(1 β) Page 4 of 14

5 R 1 βγ R 1, R 2 1 γ(1 β) R 2, 1 i 1 1 R 3 1 γ(1 β) R 3. Whereas x, y, z are lead variables, while is free variable. Thus we have x + i() = x = ct, (9) y =, z =. Hence the eigenvector associated with λ 1 for x-direction is v = ct. Substitute λ 4 into Eq.(7) yields the eigenvector associated with λ 4 for x-direction: γ γ(1 + β) iβγ 1 γ(1 + β) 1 γ(1 + β) iβγ γ γ(1 + β) βγ iβγ 1 γ(1 + β) 1 γ(1 + β) iβγ βγ ( i (R 1 )) + R 4 R 4 βγ iβγ 1 γ(1 + β) 1 γ(1 + β) R 1 βγ R 1, R 2 1 γ(1+β) R 2, 1 i 1 1 R 3 1 γ(1+β) R 3. Page 5 of 14

6 Whereas x, y, z are lead variables, while is free variable. Thus we have x i() = x = ct, (1) y =, z =. Hence the eigenvector associated with λ 4 for x-direction is w = ct. Substitute λ 2 or λ 3 into Eq.(7) yields the eigenvector associated with λ 2 and λ 3 for x- direction: γ 1 iβγ iβγ γ 1 R 1 γ 1 R 1, R 4 iβγ R 4. 1 (iβγ/γ 1) 1 (γ 1/iβγ) R 1 R 2 R 2, 1 (iβγ/γ 1) (iβγ/γ 1) + (γ 1/iβγ) R 4 (iβγ/γ 1)+(γ 1/iβγ) R 4. 1 (iβγ/γ 1) 1 The lead variables are x, and the free variable are y, z. Therefore we find =, x + (iβγ/γ 1)() = x =. (11) Page 6 of 14

7 Consequently, the eigenvectors associated with λ 2 and λ 3 are y y u = = +. z z Thus the eigenvectors matrix for the x-direction: M x = x ct ct y y. (12) z z Similarly, for the y-direction (Eq.(3)) we have M y = x x y ct ct. z z and for the z-direction (Eq.(4)): M z = x x y y. z ct ct Taking the determinants to eigenvectors matrices one finds det(v x ), det(v y ), det(v z ). Which proof the linear independency and the orthogonality of the eigenvectors. verifies that the Lorentz matrix is diagonalizable, that is Also it M 1 ΛM = D. Where M is the eigenvectors matrix, M 1 the inverse of eigenvector matrix, Λ the Lorentz matrix, D the diagonal matrix. Finding the inverse of M x : ct ct y z Page 7 of 14

8 R 1 ct R 1, R 2 y R 2, R 3 z R 3, R 4 R 4. 1/ct 1/y 1/z 1/ R 1 R 4 R /ct 1/y 1/z 1/ct 1/ R 4 2 R /ct 1/y 1/z 1/2ct 1/2 R 1 + R 4 R /2ct 1/2 1/y 1/z 1/2ct 1/2 Hence the inverse of M x is M 1 x = The inverse of M y : M 1 y = The inverse of M z : M 1 z = 1/2ct 1/2 1/y 1/z 1/2ct 1/2 1/x 1/2ct 1/2 1/z 1/2ct 1/2 1/x 1/y 1/2ct 1/2 1/2ct 1/2 Page 8 of 14

9 Again finding the inverse of the eigenvectors matrices assure its linear independency and its orthogonality. Hence the diagonal matrix for x-direction: D x = M 1 x Λ x M x, Then 1/2ct 1/2 D x = 1/y 1/z 1/2ct 1/2 1/2ct 1/2 = 1/y 1/z 1/2ct 1/2 = γ(1 β) 1 1 γ(1 + β) The diagonal matrix for y-direction: D y = 1 γ(1 β) 1 γ(1 + β) The diagonal matrix for z-direction: D z = 1 1 γ(1 β) γ(1 + β) = γ iβγ 1 1 iβγ γ ct ct y z γ(1 β)ct γ(1 + β)ct y z γ(1 β) γ(1 + β) λ 1 λ 2 λ 3 λ 4 Thus the diagonal matrix gives the same eigenvalues λ 1, λ 2, λ 3, λ 4 which farther verifies our findings. 2.3 The Doppler Transformations After we have verified the validity of deriving eigenvalues from Lorentz matrix, now becomes legal to write it in the form of equation (6), that is Λ(v)X = λ i X, i=1,2,3,4. (13) Page 9 of 14

10 Where λ 1 = γ(1 β), λ 2 = 1, λ 3 = 1, λ 4 = γ(1 + β). Multiply the R.H.S by the identity matrix yields λ i IX = DX, where I is the identity matrix and D is diagonal matrix. Therefore, equation (13) becomes Λ(v)X = DX. (14) From here on, the R.H.S of Eq.(14), and by mathematical necessity, is valid to replace the L.H.S. λ i x DX = λ i y λ i z i=1,2,3,4. λ i Let us define a function µ(+v) = γ(1 + β), then we have µ( v) = γ(1 β) and µ() = 1. The eigenvalue µ(+v) designates receding frame of reference, the eigenvalue µ( v) designates approaching frame of reference, while the eigenvalue µ() designates motionless frame of reference. Hence a general form of transformation matrix can be defined: D(v) = µ(v x ) µ(v y ) µ(v z ) µ(v) (15) where D is the equivalent transformation matrix, v the relative velocity, v x the relative velocity component in x-direction, v y the relative velocity component in y-direction, v z the relative velocity component in z-direction. Because the equivalent transformation matrix D transfers time and space in Dopplerian manner, we are going to name it the Doppler matrix and the factor µ the Doppler factor. Thus the Doppler transformation can be defined: x y z = and as matrix equation: µ(v x ) µ(v y ) µ(v z ) µ(v) x y z D(v)X = X. (16) Consequently, the Doppler transformations in x-direction: x γ(1 ± β) x y z = 1 y 1 z γ(1 ± β) Page 1 of 14

11 and as matrix equation: D x (v)x = X. where v y = v z = and v = v x. Finding the matrix multiplication one obtains the Doppler transformations equations which equivalent to the Lorentz transformations equations (Eqs.(1)): t = γ(1 ± β) t x = γ(1 ± β) x y = y z = z (17) The Doppler matrix works equally well in the y or z directions instead of x, and so the results are similar. For the y-direction: x y z = and as matrix equation: D y (v)x = X. 1 γ(1 ± β) 1 γ(1 ± β) where v x = v z = and v = v y. For the z-direction: x y z = and as matrix equation: D z (v)x = X. 1 1 γ(1 ± β) γ(1 ± β) where v x = v y = and v = v z. From Doppler matrices and its equations one recognizes the Doppler factor µ works as the relativistic factor instead of the Lorentz factor γ. x y z x y z Page 11 of 14

12 2.4 The Equivalence of The Lorentz Transformations and The Doppler transformations The Lorentz transformation is equivalent to the Doppler transformation but under the condition, that is the spatial coordinate (along the relative motion) is dependent upon the temporal coordinate. That is, if we arbitrarily choose the value of temporal coordinate then the spatial coordinate will not. The spatial and temporal coordinates are related through the constant c, the speed of light. To prove equivalence under this condition let us take the Lorentz transformations equations (Eq.(1)), the transformation for x-direction, therefore we have ( t = γ t vx ) c 2 But from Eqs.(9) and (1) we find the spatial variable x is related to the temporal variable t through the equation x = ct and x = ct, whereas x is lead variable while t is free variable. Thus substitute x = ct, we get ( t = γ t v(ct) ) c ( 2 = γ t vt ) c ( = γ 1 v c = γ (1 β) t ) t For x = ct, we obtain t = γ (1 + β) t For spatial transformation, if we choose x = ct, one obtains x = γ (x vt) ( ( x )) = γ x v c = γ (1 β) x For x = ct, we obtain x = γ (1 + β) x which definitely are the Doppler transformations for x-direction (see Eq.(17)). For x = (Eq.(11)), it is implies that t = /c =, therefore t = and x =, That is [ ] t v(x = ) = γ (t = ) =. c 2 x = γ [(x = ) v(t = )] =. Page 12 of 14

13 The absence of understanding this fact; the spatial and temporal coordinates are related through the constant c (also see Eq.(19)), led to great mistake, where in classical derivation of time dilation and length contraction formulae from the Lorentz transformations the substitution of zero into spatial or temporal coordinate has nothing to do with each other, that is [ ] t = γ t + v( x = ) = γ t, c 2 Time dilation formula. x = γ [ x v( t = )] = γ x and x = x γ, Length contraction formula. Noticing that; x = x 1 x 2 = x 2 = x 1 and because of the fact that x = ct then also t 1 = t 2 and t = t 2 t 1 =. 2.5 The Invariance of The Speed of Light and Its Consequences From equations (17), when we divide the temporal transformation equation into spatial transformation equation one discovers x t = γ(1 ± β) x γ(1 ± β) t x t = x t = const. (18) Let us denote this constant with ɛ. To find this constant we divide the Lorentz temporal transformation equation into the Lorentz spatial transformation equation (see Eqs.(1)). Remember the Lorentz and Doppler transformations equations are equivalent (see Eqs.(13) and (14)). Therefore we have ( x ) t x t = γ(x vt) ( γ t vx ) = c 2 v ( v ) ( x ) = ɛ, 1 c 2 t but from Eq.(18) we have x /t = x/t = ɛ, then ɛ = ɛ v 1 vɛ c 2, cross multiply ɛ vɛ2 = ɛ v which implies ɛ = c. (19) c2 Thus, equation (18) gives x = ct and x = ct, which implies (2a) dx/dt = c and dx /dt = c, (2b) d 2 x/dt 2 = and d 2 x /dt 2 =. (2c) These results (Eqs.(2a),(2b) and (2c)) invalidate the classical derivation of the relativistic velocity transformations and the relativistic acceleration transformations from the Lorentz transformations. Page 13 of 14

14 CONCLUSION We conclude that the spatial and temporal coordinates transfer from inertial frame of reference to another like wavelength and period consecutively, which gives clue that the spacetime is wavy space and transfers like electromagnetic waves, that is, it transfers through Doppler transformations. We also found that the relativistic factor is a function in velocity magnitude and direction (plus or minus), and we found the relativistic effect to be asymmetric along the direction of motion, that is, the approaching frame of reference and receding frame of reference are not equal relativistically; observers whom observe approaching frames of references measure (simultaneously) contraction in spatial length and temporal length, and observers whom observe receding frames of references measure (simultaneously) expansion in spatial and temporal length. We also found that the space and time transfer in manner that conserves the ratio between them, the speed of light. Therefore the spatial and temporal dimensions expand and contract simultaneously to keep the constant c invariant. We also found that due to the invariance of the speed of light (the division of time into space is equal to the speed of light) implies a fundamental relativistic kinematics formulae to be invalid, which are the relativistic velocity transformation and the relativistic acceleration transformation. Page 14 of 14

(Dated: September 3, 2015)

(Dated: September 3, 2015) LORENTZ-FITZGERALD LENGTH CONTRACTION DUE TO DOPPLER FACTOR Louai Hassan Elzein Basheir 1 Physics College, Khartoum University, Sudan. P.O. Box: 7725 - Zip Code: 11123 (Dated: September 3, 2015) This paper

More information

Simultaneity, Time Dilation, and Length Contraction Using Minkowski Diagrams and Lorentz Transformations

Simultaneity, Time Dilation, and Length Contraction Using Minkowski Diagrams and Lorentz Transformations Simultaneity, Time Dilation, and Length Contraction Using Minkowski Diagrams and Lorentz Transformations Dr. Russell L. Herman January 25, 2008 (modified: January 17, 2018) Abstract In these notes we present

More information

Module II: Relativity and Electrodynamics

Module II: Relativity and Electrodynamics Module II: Relativity and Electrodynamics Lecture 2: Lorentz transformations of observables Amol Dighe TIFR, Mumbai Outline Length, time, velocity, acceleration Transformations of electric and magnetic

More information

MIT Course 8.033, Fall 2006, Relativistic Kinematics Max Tegmark Last revised October

MIT Course 8.033, Fall 2006, Relativistic Kinematics Max Tegmark Last revised October MIT Course 8.33, Fall 6, Relativistic Kinematics Max Tegmark Last revised October 17 6 Topics Lorentz transformations toolbox formula summary inverse composition (v addition) boosts as rotations the invariant

More information

Special Relativity-General Discussion

Special Relativity-General Discussion Chapter 1 Special Relativity-General Discussion Let us consider a spacetime event. By this we mean a physical occurence at some point in space at a given time. in order to characterize this event we introduce

More information

Special Relativity-General Discussion

Special Relativity-General Discussion Chapter 1 Special Relativity-General Discussion Let us consider a space-time event. By this we mean a physical occurence at some point in space at a given time. In order to characterize this event we introduce

More information

3.1 Transformation of Velocities

3.1 Transformation of Velocities 3.1 Transformation of Velocities To prepare the way for future considerations of particle dynamics in special relativity, we need to explore the Lorentz transformation of velocities. These are simply derived

More information

Definition (T -invariant subspace) Example. Example

Definition (T -invariant subspace) Example. Example Eigenvalues, Eigenvectors, Similarity, and Diagonalization We now turn our attention to linear transformations of the form T : V V. To better understand the effect of T on the vector space V, we begin

More information

PY 351 Modern Physics Short assignment 4, Nov. 9, 2018, to be returned in class on Nov. 15.

PY 351 Modern Physics Short assignment 4, Nov. 9, 2018, to be returned in class on Nov. 15. PY 351 Modern Physics Short assignment 4, Nov. 9, 2018, to be returned in class on Nov. 15. You may write your answers on this sheet or on a separate paper. Remember to write your name on top. Please note:

More information

Lecture 15, 16: Diagonalization

Lecture 15, 16: Diagonalization Lecture 15, 16: Diagonalization Motivation: Eigenvalues and Eigenvectors are easy to compute for diagonal matrices. Hence, we would like (if possible) to convert matrix A into a diagonal matrix. Suppose

More information

Gravitation: Special Relativity

Gravitation: Special Relativity An Introduction to General Relativity Center for Relativistic Astrophysics School of Physics Georgia Institute of Technology Notes based on textbook: Spacetime and Geometry by S.M. Carroll Spring 2013

More information

Tech Notes 4 and 5. Let s prove that invariance of the spacetime interval the hard way. Suppose we begin with the equation

Tech Notes 4 and 5. Let s prove that invariance of the spacetime interval the hard way. Suppose we begin with the equation Tech Notes 4 and 5 Tech Notes 4 and 5 Let s prove that invariance of the spacetime interval the hard way. Suppose we begin with the equation (ds) 2 = (dt) 2 (dx) 2. We recall that the coordinate transformations

More information

Eigenpairs and Diagonalizability Math 401, Spring 2010, Professor David Levermore

Eigenpairs and Diagonalizability Math 401, Spring 2010, Professor David Levermore Eigenpairs and Diagonalizability Math 40, Spring 200, Professor David Levermore Eigenpairs Let A be an n n matrix A number λ possibly complex even when A is real is an eigenvalue of A if there exists a

More information

Eigenvalues and Eigenvectors

Eigenvalues and Eigenvectors Eigenvalues and Eigenvectors week -2 Fall 26 Eigenvalues and eigenvectors The most simple linear transformation from R n to R n may be the transformation of the form: T (x,,, x n ) (λ x, λ 2,, λ n x n

More information

235 Final exam review questions

235 Final exam review questions 5 Final exam review questions Paul Hacking December 4, 0 () Let A be an n n matrix and T : R n R n, T (x) = Ax the linear transformation with matrix A. What does it mean to say that a vector v R n is an

More information

MATH 221, Spring Homework 10 Solutions

MATH 221, Spring Homework 10 Solutions MATH 22, Spring 28 - Homework Solutions Due Tuesday, May Section 52 Page 279, Problem 2: 4 λ A λi = and the characteristic polynomial is det(a λi) = ( 4 λ)( λ) ( )(6) = λ 6 λ 2 +λ+2 The solutions to the

More information

MATH 423 Linear Algebra II Lecture 33: Diagonalization of normal operators.

MATH 423 Linear Algebra II Lecture 33: Diagonalization of normal operators. MATH 423 Linear Algebra II Lecture 33: Diagonalization of normal operators. Adjoint operator and adjoint matrix Given a linear operator L on an inner product space V, the adjoint of L is a transformation

More information

Remark By definition, an eigenvector must be a nonzero vector, but eigenvalue could be zero.

Remark By definition, an eigenvector must be a nonzero vector, but eigenvalue could be zero. Sec 6 Eigenvalues and Eigenvectors Definition An eigenvector of an n n matrix A is a nonzero vector x such that A x λ x for some scalar λ A scalar λ is called an eigenvalue of A if there is a nontrivial

More information

Fundamental equations of relativistic fluid dynamics

Fundamental equations of relativistic fluid dynamics CHAPTER VI Fundamental equations of relativistic fluid dynamics When the energy density becomes large as may happen for instance in compact astrophysical objects, in the early Universe, or in high-energy

More information

Jordan Canonical Form Homework Solutions

Jordan Canonical Form Homework Solutions Jordan Canonical Form Homework Solutions For each of the following, put the matrix in Jordan canonical form and find the matrix S such that S AS = J. [ ]. A = A λi = λ λ = ( λ) = λ λ = λ =, Since we have

More information

George Mason University. Physics 540 Spring Notes on Relativistic Kinematics. 1 Introduction 2

George Mason University. Physics 540 Spring Notes on Relativistic Kinematics. 1 Introduction 2 George Mason University Physics 540 Spring 2011 Contents Notes on Relativistic Kinematics 1 Introduction 2 2 Lorentz Transformations 2 2.1 Position-time 4-vector............................. 3 2.2 Velocity

More information

PH.D. PRELIMINARY EXAMINATION MATHEMATICS

PH.D. PRELIMINARY EXAMINATION MATHEMATICS UNIVERSITY OF CALIFORNIA, BERKELEY SPRING SEMESTER 207 Dept. of Civil and Environmental Engineering Structural Engineering, Mechanics and Materials NAME PH.D. PRELIMINARY EXAMINATION MATHEMATICS Problem

More information

Diagonalization of Matrix

Diagonalization of Matrix of Matrix King Saud University August 29, 2018 of Matrix Table of contents 1 2 of Matrix Definition If A M n (R) and λ R. We say that λ is an eigenvalue of the matrix A if there is X R n \ {0} such that

More information

Therefore F = ma = ma = F So both observers will not only agree on Newton s Laws, but will agree on the value of F.

Therefore F = ma = ma = F So both observers will not only agree on Newton s Laws, but will agree on the value of F. Classical Physics Inertial Reference Frame (Section 5.2): a reference frame in which an object obeys Newton s Laws, i.e. F = ma and if F = 0 (object does not interact with other objects), its velocity

More information

1 Tensors and relativity

1 Tensors and relativity Physics 705 1 Tensors and relativity 1.1 History Physical laws should not depend on the reference frame used to describe them. This idea dates back to Galileo, who recognized projectile motion as free

More information

MATH 304 Linear Algebra Lecture 23: Diagonalization. Review for Test 2.

MATH 304 Linear Algebra Lecture 23: Diagonalization. Review for Test 2. MATH 304 Linear Algebra Lecture 23: Diagonalization. Review for Test 2. Diagonalization Let L be a linear operator on a finite-dimensional vector space V. Then the following conditions are equivalent:

More information

The Lorentz Transformation from Light-Speed Invariance Alone

The Lorentz Transformation from Light-Speed Invariance Alone The Lorentz Transformation from Light-Speed Invariance Alone Steven Kenneth Kauffmann Abstract The derivation of the Lorentz transformation normally rests on two a priori demands namely that reversing

More information

Symmetric and anti symmetric matrices

Symmetric and anti symmetric matrices Symmetric and anti symmetric matrices In linear algebra, a symmetric matrix is a square matrix that is equal to its transpose. Formally, matrix A is symmetric if. A = A Because equal matrices have equal

More information

Math 3191 Applied Linear Algebra

Math 3191 Applied Linear Algebra Math 9 Applied Linear Algebra Lecture 9: Diagonalization Stephen Billups University of Colorado at Denver Math 9Applied Linear Algebra p./9 Section. Diagonalization The goal here is to develop a useful

More information

ACM 104. Homework Set 5 Solutions. February 21, 2001

ACM 104. Homework Set 5 Solutions. February 21, 2001 ACM 04 Homework Set 5 Solutions February, 00 Franklin Chapter 4, Problem 4, page 0 Let A be an n n non-hermitian matrix Suppose that A has distinct eigenvalues λ,, λ n Show that A has the eigenvalues λ,,

More information

Quantum Field Theory Notes. Ryan D. Reece

Quantum Field Theory Notes. Ryan D. Reece Quantum Field Theory Notes Ryan D. Reece November 27, 2007 Chapter 1 Preliminaries 1.1 Overview of Special Relativity 1.1.1 Lorentz Boosts Searches in the later part 19th century for the coordinate transformation

More information

Remark 1 By definition, an eigenvector must be a nonzero vector, but eigenvalue could be zero.

Remark 1 By definition, an eigenvector must be a nonzero vector, but eigenvalue could be zero. Sec 5 Eigenvectors and Eigenvalues In this chapter, vector means column vector Definition An eigenvector of an n n matrix A is a nonzero vector x such that A x λ x for some scalar λ A scalar λ is called

More information

What s Eigenanalysis? Matrix eigenanalysis is a computational theory for the matrix equation

What s Eigenanalysis? Matrix eigenanalysis is a computational theory for the matrix equation Eigenanalysis What s Eigenanalysis? Fourier s Eigenanalysis Model is a Replacement Process Powers and Fourier s Model Differential Equations and Fourier s Model Fourier s Model Illustrated What is Eigenanalysis?

More information

Lorentz Transformations and Special Relativity

Lorentz Transformations and Special Relativity Lorentz Transformations and Special Relativity Required reading: Zwiebach 2.,2,6 Suggested reading: Units: French 3.7-0, 4.-5, 5. (a little less technical) Schwarz & Schwarz.2-6, 3.-4 (more mathematical)

More information

Primer in Special Relativity and Electromagnetic Equations (Lecture 13)

Primer in Special Relativity and Electromagnetic Equations (Lecture 13) Primer in Special Relativity and Electromagnetic Equations (Lecture 13) January 29, 2016 212/441 Lecture outline We will review the relativistic transformation for time-space coordinates, frequency, and

More information

Particle Notes. Ryan D. Reece

Particle Notes. Ryan D. Reece Particle Notes Ryan D. Reece July 9, 2007 Chapter 1 Preliminaries 1.1 Overview of Special Relativity 1.1.1 Lorentz Boosts Searches in the later part 19th century for the coordinate transformation that

More information

Overthrows a basic assumption of classical physics - that lengths and time intervals are absolute quantities, i.e., the same for all observes.

Overthrows a basic assumption of classical physics - that lengths and time intervals are absolute quantities, i.e., the same for all observes. Relativistic Electrodynamics An inertial frame = coordinate system where Newton's 1st law of motion - the law of inertia - is true. An inertial frame moves with constant velocity with respect to any other

More information

Announcements Wednesday, November 7

Announcements Wednesday, November 7 Announcements Wednesday, November 7 The third midterm is on Friday, November 6 That is one week from this Friday The exam covers 45, 5, 52 53, 6, 62, 64, 65 (through today s material) WeBWorK 6, 62 are

More information

Massachusetts Institute of Technology Physics Department

Massachusetts Institute of Technology Physics Department Massachusetts Institute of Technology Physics Department Physics 8.20 IAP 2003 Introduction to Special Relativity January 6, 2003 Assignment 1 Corrected version Due January 13, 2003 Announcements Please

More information

Recall : Eigenvalues and Eigenvectors

Recall : Eigenvalues and Eigenvectors Recall : Eigenvalues and Eigenvectors Let A be an n n matrix. If a nonzero vector x in R n satisfies Ax λx for a scalar λ, then : The scalar λ is called an eigenvalue of A. The vector x is called an eigenvector

More information

Eigenvalues. Matrices: Geometric Interpretation. Calculating Eigenvalues

Eigenvalues. Matrices: Geometric Interpretation. Calculating Eigenvalues Eigenvalues Matrices: Geometric Interpretation Start with a vector of length 2, for example, x =(1, 2). This among other things give the coordinates for a point on a plane. Take a 2 2 matrix, for example,

More information

q n. Q T Q = I. Projections Least Squares best fit solution to Ax = b. Gram-Schmidt process for getting an orthonormal basis from any basis.

q n. Q T Q = I. Projections Least Squares best fit solution to Ax = b. Gram-Schmidt process for getting an orthonormal basis from any basis. Exam Review Material covered by the exam [ Orthogonal matrices Q = q 1... ] q n. Q T Q = I. Projections Least Squares best fit solution to Ax = b. Gram-Schmidt process for getting an orthonormal basis

More information

FYS 3120: Classical Mechanics and Electrodynamics

FYS 3120: Classical Mechanics and Electrodynamics FYS 3120: Classical Mechanics and Electrodynamics Formula Collection Spring semester 2014 1 Analytical Mechanics The Lagrangian L = L(q, q, t), (1) is a function of the generalized coordinates q = {q i

More information

Minkowski spacetime. Pham A. Quang. Abstract: In this talk we review the Minkowski spacetime which is the spacetime of Special Relativity.

Minkowski spacetime. Pham A. Quang. Abstract: In this talk we review the Minkowski spacetime which is the spacetime of Special Relativity. Minkowski spacetime Pham A. Quang Abstract: In this talk we review the Minkowski spacetime which is the spacetime of Special Relativity. Contents 1 Introduction 1 2 Minkowski spacetime 2 3 Lorentz transformations

More information

Announcements Wednesday, November 7

Announcements Wednesday, November 7 Announcements Wednesday, November 7 The third midterm is on Friday, November 16 That is one week from this Friday The exam covers 45, 51, 52 53, 61, 62, 64, 65 (through today s material) WeBWorK 61, 62

More information

LINEAR ALGEBRA 1, 2012-I PARTIAL EXAM 3 SOLUTIONS TO PRACTICE PROBLEMS

LINEAR ALGEBRA 1, 2012-I PARTIAL EXAM 3 SOLUTIONS TO PRACTICE PROBLEMS LINEAR ALGEBRA, -I PARTIAL EXAM SOLUTIONS TO PRACTICE PROBLEMS Problem (a) For each of the two matrices below, (i) determine whether it is diagonalizable, (ii) determine whether it is orthogonally diagonalizable,

More information

Study Guide for Linear Algebra Exam 2

Study Guide for Linear Algebra Exam 2 Study Guide for Linear Algebra Exam 2 Term Vector Space Definition A Vector Space is a nonempty set V of objects, on which are defined two operations, called addition and multiplication by scalars (real

More information

Answer Keys For Math 225 Final Review Problem

Answer Keys For Math 225 Final Review Problem Answer Keys For Math Final Review Problem () For each of the following maps T, Determine whether T is a linear transformation. If T is a linear transformation, determine whether T is -, onto and/or bijective.

More information

MATH 583A REVIEW SESSION #1

MATH 583A REVIEW SESSION #1 MATH 583A REVIEW SESSION #1 BOJAN DURICKOVIC 1. Vector Spaces Very quick review of the basic linear algebra concepts (see any linear algebra textbook): (finite dimensional) vector space (or linear space),

More information

1. Matrix multiplication and Pauli Matrices: Pauli matrices are the 2 2 matrices. 1 0 i 0. 0 i

1. Matrix multiplication and Pauli Matrices: Pauli matrices are the 2 2 matrices. 1 0 i 0. 0 i Problems in basic linear algebra Science Academies Lecture Workshop at PSGRK College Coimbatore, June 22-24, 2016 Govind S. Krishnaswami, Chennai Mathematical Institute http://www.cmi.ac.in/~govind/teaching,

More information

Econ Slides from Lecture 7

Econ Slides from Lecture 7 Econ 205 Sobel Econ 205 - Slides from Lecture 7 Joel Sobel August 31, 2010 Linear Algebra: Main Theory A linear combination of a collection of vectors {x 1,..., x k } is a vector of the form k λ ix i for

More information

4 Matrix Diagonalization and Eigensystems

4 Matrix Diagonalization and Eigensystems 14.102, Math for Economists Fall 2004 Lecture Notes, 9/21/2004 These notes are primarily based on those written by George Marios Angeletos for the Harvard Math Camp in 1999 and 2000, and updated by Stavros

More information

Vectors To begin, let us describe an element of the state space as a point with numerical coordinates, that is x 1. x 2. x =

Vectors To begin, let us describe an element of the state space as a point with numerical coordinates, that is x 1. x 2. x = Linear Algebra Review Vectors To begin, let us describe an element of the state space as a point with numerical coordinates, that is x 1 x x = 2. x n Vectors of up to three dimensions are easy to diagram.

More information

Section 7.3: SYMMETRIC MATRICES AND ORTHOGONAL DIAGONALIZATION

Section 7.3: SYMMETRIC MATRICES AND ORTHOGONAL DIAGONALIZATION Section 7.3: SYMMETRIC MATRICES AND ORTHOGONAL DIAGONALIZATION When you are done with your homework you should be able to Recognize, and apply properties of, symmetric matrices Recognize, and apply properties

More information

Mathematical foundations - linear algebra

Mathematical foundations - linear algebra Mathematical foundations - linear algebra Andrea Passerini passerini@disi.unitn.it Machine Learning Vector space Definition (over reals) A set X is called a vector space over IR if addition and scalar

More information

Lecture 12 Notes, Electromagnetic Theory II Dr. Christopher S. Baird, faculty.uml.edu/cbaird University of Massachusetts Lowell

Lecture 12 Notes, Electromagnetic Theory II Dr. Christopher S. Baird, faculty.uml.edu/cbaird University of Massachusetts Lowell Lecture 12 Notes, Electromagnetic Theory II Dr. Christopher S. Baird, faculty.uml.edu/cbaird University of Massachusetts Lowell 1. Velocities in Special Relativity - As was done in Galilean relativity,

More information

Math 108b: Notes on the Spectral Theorem

Math 108b: Notes on the Spectral Theorem Math 108b: Notes on the Spectral Theorem From section 6.3, we know that every linear operator T on a finite dimensional inner product space V has an adjoint. (T is defined as the unique linear operator

More information

Practice Final Exam Solutions for Calculus II, Math 1502, December 5, 2013

Practice Final Exam Solutions for Calculus II, Math 1502, December 5, 2013 Practice Final Exam Solutions for Calculus II, Math 5, December 5, 3 Name: Section: Name of TA: This test is to be taken without calculators and notes of any sorts. The allowed time is hours and 5 minutes.

More information

MTH 102: Linear Algebra Department of Mathematics and Statistics Indian Institute of Technology - Kanpur. Problem Set

MTH 102: Linear Algebra Department of Mathematics and Statistics Indian Institute of Technology - Kanpur. Problem Set MTH 102: Linear Algebra Department of Mathematics and Statistics Indian Institute of Technology - Kanpur Problem Set 6 Problems marked (T) are for discussions in Tutorial sessions. 1. Find the eigenvalues

More information

Eigenvalues and Eigenvectors

Eigenvalues and Eigenvectors /88 Chia-Ping Chen Department of Computer Science and Engineering National Sun Yat-sen University Linear Algebra Eigenvalue Problem /88 Eigenvalue Equation By definition, the eigenvalue equation for matrix

More information

Newtonian or Galilean Relativity

Newtonian or Galilean Relativity Relativity Eamples 1. What is the velocity of an electron in a 400 kv transmission electron microscope? What is the velocity in the 6 GeV CESR particle accelerator?. If one million muons enter the atmosphere

More information

= m(v) X B = m(0) 0 + m(v) x B m(0) + m(v) u = dx B dt B. m + m(v) v. 2u 1 + v A u/c 2 = v = 1 + v2. c 2 = 0

= m(v) X B = m(0) 0 + m(v) x B m(0) + m(v) u = dx B dt B. m + m(v) v. 2u 1 + v A u/c 2 = v = 1 + v2. c 2 = 0 7 Relativistic dynamics Lorentz transformations also aect the accelerated motion of objects under the inuence of forces. In Newtonian physics a constant force F accelerates an abject at a constant rate

More information

Exercise Set 7.2. Skills

Exercise Set 7.2. Skills Orthogonally diagonalizable matrix Spectral decomposition (or eigenvalue decomposition) Schur decomposition Subdiagonal Upper Hessenburg form Upper Hessenburg decomposition Skills Be able to recognize

More information

The Spine Model: Relativistic Generalization to any N-dimensional spacetime

The Spine Model: Relativistic Generalization to any N-dimensional spacetime Published in the New Age Journal of Physics www.cthepjo.wordpress.com Copyright 2013, Shreyak Chakraborty The Spine Model: Relativistic Generalization to any N-dimensional spacetime Shreyak Chakraborty

More information

The Lorentz Transformation

The Lorentz Transformation The Lorentz Transformation During the fourth week of the course, we spent some time discussing how the coordinates of two different reference frames were related to each other. Now that we know about the

More information

Covariant Formulation of Electrodynamics

Covariant Formulation of Electrodynamics Chapter 7. Covariant Formulation of Electrodynamics Notes: Most of the material presented in this chapter is taken from Jackson, Chap. 11, and Rybicki and Lightman, Chap. 4. Starting with this chapter,

More information

18.06 Professor Strang Quiz 3 May 2, 2008

18.06 Professor Strang Quiz 3 May 2, 2008 18.06 Professor Strang Quiz 3 May 2, 2008 Your PRINTED name is: Grading 1 2 3 Please circle your recitation: 1) M 2 2-131 A. Ritter 2-085 2-1192 afr 2) M 2 4-149 A. Tievsky 2-492 3-4093 tievsky 3) M 3

More information

2.4 The Lorentz Transformation

2.4 The Lorentz Transformation Announcement Course webpage http://highenergy.phys.ttu.edu/~slee/2402/ Textbook PHYS-2402 Lecture 4 Jan. 27, 2015 Lecture Notes, HW Assignments, Physics Colloquium, etc.. 2.4 The Lorentz Transformation

More information

The spacetime of special relativity

The spacetime of special relativity 1 The spacetime of special relativity We begin our discussion of the relativistic theory of gravity by reviewing some basic notions underlying the Newtonian and special-relativistic viewpoints of space

More information

Vectors, matrices, eigenvalues and eigenvectors

Vectors, matrices, eigenvalues and eigenvectors Vectors, matrices, eigenvalues and eigenvectors 1 ( ) ( ) ( ) Scaling a vector: 0.5V 2 0.5 2 1 = 0.5 = = 1 0.5 1 0.5 ( ) ( ) ( ) ( ) Adding two vectors: V + W 2 1 2 + 1 3 = + = = 1 3 1 + 3 4 ( ) ( ) a

More information

This is a closed book exam. No notes or calculators are permitted. We will drop your lowest scoring question for you.

This is a closed book exam. No notes or calculators are permitted. We will drop your lowest scoring question for you. Math 54 Fall 2017 Practice Final Exam Exam date: 12/14/17 Time Limit: 170 Minutes Name: Student ID: GSI or Section: This exam contains 9 pages (including this cover page) and 10 problems. Problems are

More information

Summer Session Practice Final Exam

Summer Session Practice Final Exam Math 2F Summer Session 25 Practice Final Exam Time Limit: Hours Name (Print): Teaching Assistant This exam contains pages (including this cover page) and 9 problems. Check to see if any pages are missing.

More information

MATH 56A: STOCHASTIC PROCESSES CHAPTER 0

MATH 56A: STOCHASTIC PROCESSES CHAPTER 0 MATH 56A: STOCHASTIC PROCESSES CHAPTER 0 0. Chapter 0 I reviewed basic properties of linear differential equations in one variable. I still need to do the theory for several variables. 0.1. linear differential

More information

x α x β g α β = xα x β g αβ. (1.1)

x α x β g α β = xα x β g αβ. (1.1) Physics 445 Solution for homework 4 Fall Cornell University NOTE We use the notion where four-vectors v are denoted by an arrow, and three-vectors v will be in bold. Hartle uses the opposite notation.

More information

A Theory of Special Relativity Based on Four-Displacement of Particles Instead of Minkowski Four-Position

A Theory of Special Relativity Based on Four-Displacement of Particles Instead of Minkowski Four-Position A Theory of Special Relativity Based on Four-Displacement of Particles Instead of Minkowski Four-Position Albert Zur (Albo) Ph.D. Tel Aviv University Raanana, Israel e-mail address: albert@gality.com ABSTRACT

More information

Lecture 3 Eigenvalues and Eigenvectors

Lecture 3 Eigenvalues and Eigenvectors Lecture 3 Eigenvalues and Eigenvectors Eivind Eriksen BI Norwegian School of Management Department of Economics September 10, 2010 Eivind Eriksen (BI Dept of Economics) Lecture 3 Eigenvalues and Eigenvectors

More information

Superluminal motion in the quasar 3C273

Superluminal motion in the quasar 3C273 1 Superluminal motion in the quasar 3C273 The cowboys have a way of trussing up a steer or a pugnacious bronco which fixes the brute so that it can neither move nor think. This is the hog-tie, and it is

More information

Math Linear Algebra Final Exam Review Sheet

Math Linear Algebra Final Exam Review Sheet Math 15-1 Linear Algebra Final Exam Review Sheet Vector Operations Vector addition is a component-wise operation. Two vectors v and w may be added together as long as they contain the same number n of

More information

4-Vector Notation. Chris Clark September 5, 2006

4-Vector Notation. Chris Clark September 5, 2006 4-Vector Notation Chris Clark September 5, 2006 1 Lorentz Transformations We will assume that the reader is familiar with the Lorentz Transformations for a boost in the x direction x = γ(x vt) ȳ = y x

More information

Chapter 1. Relativity 1

Chapter 1. Relativity 1 Chapter 1 Relativity 1 Classical Relativity inertial vs noninertial reference frames Inertial Reference Frames Galilean transformation: x = x vt; y = y; z = z; t = t u x = u x v; u y = u y ; u z = u z

More information

Matrix-Exponentials. September 7, dx dt = ax. x(t) = e at x(0)

Matrix-Exponentials. September 7, dx dt = ax. x(t) = e at x(0) Matrix-Exponentials September 7, 207 In [4]: using PyPlot INFO: Recompiling stale cache file /Users/stevenj/.julia/lib/v0.5/LaTeXStrings.ji for module LaTeXString Review: Solving ODEs via eigenvectors

More information

The Special Theory of Relativity

The Special Theory of Relativity 2 The Special Theory of Relativity In this chapter we shall give a short introduction to the fundamental prin- ciples of the special theory of relativity, and deduce some of the consequences of the theory.

More information

18.06 Problem Set 8 Solution Due Wednesday, 22 April 2009 at 4 pm in Total: 160 points.

18.06 Problem Set 8 Solution Due Wednesday, 22 April 2009 at 4 pm in Total: 160 points. 86 Problem Set 8 Solution Due Wednesday, April 9 at 4 pm in -6 Total: 6 points Problem : If A is real-symmetric, it has real eigenvalues What can you say about the eigenvalues if A is real and anti-symmetric

More information

Linear Algebra: Matrix Eigenvalue Problems

Linear Algebra: Matrix Eigenvalue Problems CHAPTER8 Linear Algebra: Matrix Eigenvalue Problems Chapter 8 p1 A matrix eigenvalue problem considers the vector equation (1) Ax = λx. 8.0 Linear Algebra: Matrix Eigenvalue Problems Here A is a given

More information

Lecture 9 - Applications of 4 vectors, and some examples

Lecture 9 - Applications of 4 vectors, and some examples Lecture 9 - Applications of 4 vectors, and some examples E. Daw April 4, 211 1 Review of invariants and 4 vectors Last time we learned the formulae for the total energy and the momentum of a particle in

More information

Chapter 11. Special Relativity

Chapter 11. Special Relativity Chapter 11 Special Relativity Note: Please also consult the fifth) problem list associated with this chapter In this chapter, Latin indices are used for space coordinates only eg, i = 1,2,3, etc), while

More information

Relativistic Boats: an explanation of special relativity. Brianna Thorpe, Dr. Michael Dugger

Relativistic Boats: an explanation of special relativity. Brianna Thorpe, Dr. Michael Dugger height Relativistic Boats: an explanation of special relativity Brianna Thorpe, Dr. Michael Dugger Time Dilation Relativity is all about your point of view. We are working with the speed of light and some

More information

Physics 4183 Electricity and Magnetism II. Covariant Formulation of Electrodynamics-1

Physics 4183 Electricity and Magnetism II. Covariant Formulation of Electrodynamics-1 Physics 4183 Electricity and Magnetism II Covariant Formulation of Electrodynamics 1 Introduction Having briefly discussed the origins of relativity, the Lorentz transformations, 4-vectors and tensors,

More information

Advanced Engineering Mathematics Prof. Pratima Panigrahi Department of Mathematics Indian Institute of Technology, Kharagpur

Advanced Engineering Mathematics Prof. Pratima Panigrahi Department of Mathematics Indian Institute of Technology, Kharagpur Advanced Engineering Mathematics Prof. Pratima Panigrahi Department of Mathematics Indian Institute of Technology, Kharagpur Lecture No. #07 Jordan Canonical Form Cayley Hamilton Theorem (Refer Slide Time:

More information

Mechanics Physics 151

Mechanics Physics 151 Mechanics Physics 151 Lecture 15 Special Relativity (Chapter 7) What We Did Last Time Defined Lorentz transformation Linear transformation of 4-vectors that conserve the length in Minkowski space Derived

More information

Postulates of Special Relativity

Postulates of Special Relativity Relativity Relativity - Seen as an intricate theory that is necessary when dealing with really high speeds - Two charged initially stationary particles: Electrostatic force - In another, moving reference

More information

Linear Algebra. Rekha Santhanam. April 3, Johns Hopkins Univ. Rekha Santhanam (Johns Hopkins Univ.) Linear Algebra April 3, / 7

Linear Algebra. Rekha Santhanam. April 3, Johns Hopkins Univ. Rekha Santhanam (Johns Hopkins Univ.) Linear Algebra April 3, / 7 Linear Algebra Rekha Santhanam Johns Hopkins Univ. April 3, 2009 Rekha Santhanam (Johns Hopkins Univ.) Linear Algebra April 3, 2009 1 / 7 Dynamical Systems Denote owl and wood rat populations at time k

More information

Math 312 Final Exam Jerry L. Kazdan May 5, :00 2:00

Math 312 Final Exam Jerry L. Kazdan May 5, :00 2:00 Math 32 Final Exam Jerry L. Kazdan May, 204 2:00 2:00 Directions This exam has three parts. Part A has shorter questions, (6 points each), Part B has 6 True/False questions ( points each), and Part C has

More information

Inconsistencies in Special Relativity? Sagnac Effect and Twin Paradox

Inconsistencies in Special Relativity? Sagnac Effect and Twin Paradox Inconsistencies in Special Relativity? Sagnac Effect and Twin Paradox Olaf Wucknitz Astrophysics Seminar Potsdam University, Germany 7 July 2003 And now for something completely different... Special relativity

More information

Lecture 27: Structural Dynamics - Beams.

Lecture 27: Structural Dynamics - Beams. Chapter #16: Structural Dynamics and Time Dependent Heat Transfer. Lectures #1-6 have discussed only steady systems. There has been no time dependence in any problems. We will investigate beam dynamics

More information

Particle Physics. Michaelmas Term 2011 Prof. Mark Thomson. Handout 2 : The Dirac Equation. Non-Relativistic QM (Revision)

Particle Physics. Michaelmas Term 2011 Prof. Mark Thomson. Handout 2 : The Dirac Equation. Non-Relativistic QM (Revision) Particle Physics Michaelmas Term 2011 Prof. Mark Thomson + e - e + - + e - e + - + e - e + - + e - e + - Handout 2 : The Dirac Equation Prof. M.A. Thomson Michaelmas 2011 45 Non-Relativistic QM (Revision)

More information

Special Theory of Relativity

Special Theory of Relativity June 17, 2008 1 1 J.D.Jackson, Classical Electrodynamics, 3rd Edition, Chapter 11 Introduction Einstein s theory of special relativity is based on the assumption (which might be a deep-rooted superstition

More information

Review of Linear Algebra Definitions, Change of Basis, Trace, Spectral Theorem

Review of Linear Algebra Definitions, Change of Basis, Trace, Spectral Theorem Review of Linear Algebra Definitions, Change of Basis, Trace, Spectral Theorem Steven J. Miller June 19, 2004 Abstract Matrices can be thought of as rectangular (often square) arrays of numbers, or as

More information

Section 8.2 : Homogeneous Linear Systems

Section 8.2 : Homogeneous Linear Systems Section 8.2 : Homogeneous Linear Systems Review: Eigenvalues and Eigenvectors Let A be an n n matrix with constant real components a ij. An eigenvector of A is a nonzero n 1 column vector v such that Av

More information

Radiative Processes in Astrophysics

Radiative Processes in Astrophysics Radiative Processes in Astrophysics 6. Relativistic Covariance & Kinematics Eline Tolstoy http://www.astro.rug.nl/~etolstoy/astroa07/ Practise, practise, practise... mid-term, 31st may, 9.15-11am As we

More information