11 Tensors and Local Symmetries
11.1 Points and Coordinates A point on a curved surface or in a curved space also is a point in a higher- dimensional flat space called an embedding space. For instance, a point on a sphere also is a point in three-dimensional euclidean space and in four- dimensional spacetime. One always can add extra dimensions, but it’s sim- pler to use as few as possible, three in the case of a sphere. On a su ciently small scale, any reasonably smooth space locally looks like n-dimensional euclidean space. Such a space is called a manifold.In- cidentally, according to Whitney’s embedding theorem, every n-dimensional connected, smooth manifold can be embedded in 2n-dimensional euclidean space R2n. So the embedding space for such spaces in general relativity has no more than eight dimensions. We use coordinates to label points. For example, we can choose a polar axis and a meridian and label a point on the sphere by its polar and az- imuthal angles (✓, ) with respect to that axis and meridian. If we use a dif- ferent axis and meridian, then the coordinates (✓0, 0) for the same point will change. Points are physical, coordinates are metaphysical. When we change our system of coordinates, the points don’t change, but their coordinates do. i i Most points p have unique coordinates x (p) and x0 (p) in their coordinate systems. For instance, polar coordinates (✓, ) are unique for all points on a sphere — except the north and south poles which are labeled by ✓ =0 and ✓ = ⇡ and all 0 <2⇡. By using more than one coordinate system, one usually can arrange to label every point uniquely in some coordinate system. In the flat three-dimensional space in which the sphere is a surface, each point of the sphere has unique coordinates, p~ =(x, y, z). 11.2 Scalars 451 We will use coordinate systems that represent the points of a manifold uniquely and smoothly at least in local patches, so that the maps
i i i i x0 = x0 (p)=x0 (p(x)) = x0 (x) (11.1) and i i i i x = x (p)=x (p(x0)) = x (x0) (11.2) are well defined, di↵erentiable, and one to one in the patches. We’ll often group the n coordinates xi together and write them collectively as x without a superscript. Since the coordinates x(p) label the point p, we sometimes will call them “the point x.” But p and x are di↵erent. The point p is unique with infinitely many coordinates x, x0, x00, . . . in infinitely many coordinate systems.
11.2 Scalars A scalar is a quantity B that is the same in all coordinate systems
B0 = B. (11.3) If it also depends upon the coordinates x of the spacetime point p,thenit is a scalar field, and
B0(x0)=B(x). (11.4)
11.3 Contravariant Vectors i By the chain rule, the change in dx0 due to changes in the unprimed coor- dinates is i i @x0 j dx0 = dx . (11.5) @xj Xj This transformation defines contravariant vectors: a quantity Ai is a component of contravariant vector if it transforms like dxi i i @x0 j A0 = A . (11.6) @xj Xj The coordinate di↵erentials dxi form a contravariant vector. A contravariant vector Ai(x) that depends on the coordinates x is a contravariant vector 452 Tensors and Local Symmetries field and transforms as i i @x0 j A0 (x0)= A (x). (11.7) @xj Xj
11.4 Covariant Vectors The chain rule for partial derivatives @ @xj @ i = i j (11.8) @x0 @x0 @x Xj defines covariant vectors: a quantity Ci that transforms as @xj Ci0 = i Cj (11.9) @x0 Xj is a component of a covariant vector. A covariant vector Ci(x) that de- pends on the coordinates x and transforms as @xj Ci0(x0)= i Cj(x) (11.10) @x0 Xj is a covariant vector field.
Example 11.1 (Gradient of a Scalar). The derivatives of a scalar field B0(x0)=B(x) form a covariant vector field because j @B0(x0) @B(x) @x @B(x) i = i = i j , (11.11) @x0 @x0 @x0 @x Xj which shows that the gradient @B(x)/@xj fits the definition (11.10) of a covariant vector field.
11.5 Euclidean Space in Euclidean Coordinates If we use euclidean coordinates to describe points in euclidean space, then covariant and contravariant vectors are the same. Euclidean space has a natural inner product (section 1.6), the usual dot- product, which is real and symmetric. In a euclidean space of n dimensions, 11.5 Euclidean Space in Euclidean Coordinates 453 we may choose any n fixed, orthonormal basis vectors ei
n (e ,e ) e e = ek ek = (11.12) i j ⌘ i · j i j ij Xk=1 and use them to represent any point p as the linear combination
n i p = ei x . (11.13) Xi=1 The dual vectors ei are defined as those vectors whose inner products with the ej are n i i i (e ,ej)= ekejk = j (11.14) Xk=1 zero or one. Here they are the same as the vectors ei, and so we don’t need i i to distinguish e from ei = e , but we will anyway. i The coe cients x are the euclidean coordinates in the ei basis. Since they and the dual vectors ej are orthonormal, each xi is an inner product or dot product
n n xi = ei p = ei e xj = i xj. (11.15) · · j j Xj=1 Xj=1
If we use di↵erent orthonormal vectors ei0 as a basis n i p = ei0 x0 (11.16) Xi=1 then we get new euclidean coordinates x i = e i p for the same point p. 0 0 · These two sets of coordinates are related by the equations
n i i i j x0 = e0 p = e0 e x · · j Xj=1 n (11.17) j j j k x = e p = e e0 x0 . · · k Xk=1 i Because the dual vectors e are the same as the basis vectors ej and are i j independent of the euclidian coordinates x,thecoe cients@x0 /@x and j i @x /@x0 of the transformation laws for contravariant (11.6) and covariant 454 Tensors and Local Symmetries (11.9) vectors are the same
i n n j @x0 i i j j @x j = e0 ej = ek0 ejk = eik0 ek = e ei0 = i . (11.18) @x · · @x0 Xk=1 Xk=1 So contravariant and covariant vectors transform the same way in euclidean space with euclidean coordinates. i j The relations between x0 and x imply that
n i i j k x0 = e0 e e e0 x0 . (11.19) · j · k j,k=1 X i Since this holds for all coordinates x0 ,wehave
n i j e0 e e e0 = . (11.20) · j · k ik j=1 X The coe cients e i e = e ej form an orthogonal matrix, and the linear 0 · j i0 · operator
n n iT i e e0 = e e0 (11.21) i | iih | Xi=1 Xi=1 is an orthogonal (real, unitary) transformation. The change x x is a ! 0 rotation and/or a reflection (exercise 11.2).
Example 11.2 (A Euclidean Space of Two Dimensions). In two-dimensional euclidean space, one can describe the same point by euclidean (x, y) and polar (r, ✓) coordinates. The derivatives
@r x @x @r y @y = = and = = (11.22) @x r @r @y r @r respect the symmetry (11.18), but (exercise 11.1) the derivatives
@✓ y @x @✓ x @y = = = y and = = = x (11.23) @x r2 6 @✓ @y r2 6 @✓ do not because polar coordinates are not euclidean. 11.6 Summation convention 455 11.6 Summation convention An index that appears twice in the same monomial, once as a subscript and once as a superscript, is a dummy index that is summed over as in n A Bi A Bi. (11.24) i ⌘ i Xi=1 The sum is understood to be over the relevant range of indices, usually from 0 or 1 to 3 or n. These summation conventions make tensor notation almost as compact as matrix notation. They make equations easier to read and write.
Example 11.3 (The Kronecker Delta). The summation convention and the chain rule imply that
@x i @xk @x i 1ifi = j 0 = 0 = i = (11.25) @xk @x j @x j j 0ifi = j. 0 0 ⇢ 6 The repeated index k has disappeared in this contraction.
11.7 Minkowski Space Minkowski space has one time dimension, labeled by k = 0, and n space dimensions. In special relativity, n = 3, and the Minkowski metric ⌘ is 1ifk = ` =0 ⌘ = ⌘k` = 1if1k = ` 3 . (11.26) k` 8 0ifk = ` < 6 k It defines an inner product between: points p and q with coordinates xp and ` xq as (p, q)=p q = pk ⌘ q` =(q, p). (11.27) · k` If one time component vanishes, the Minkowski inner product reduces to the euclidean dot-product (1.82). We can use di↵erent sets e and e of n +1Lorentz-orthonormal { i} { i0 } basis vectors
k ` (e ,e )=e e = e ⌘ e = ⌘ = e0 e0 =(e0 ,e0 ) (11.28) i j i · j i k` j ij i · j i j to represent any point p in the space either as a linear combination of the 456 Tensors and Local Symmetries
i vectors ei with coe cients x or as a linear combination of the vectors ei0 i with coe cients x0 i i p = ei x = ei0 x0 . (11.29) The dual vectors, which carry upper indices, are defined as
i ij i ij e = ⌘ ej and e0 = ⌘ ej0 . (11.30)
They are orthonormal to the vectors ei and ei0 because (ei,e )=ei e = ⌘ik e e = ⌘ik ⌘ = i (11.31) j · j k · j kj j i i i and similarly (e0 ,ej0 )=e0 ej0 = j. Since the square of the matrix ⌘ is the ij j · identity matrix ⌘`i⌘ = ` , it follows that j j ei = ⌘ij e and ei0 = ⌘ij e0 . (11.32) The metric ⌘ raises (11.30) and lowers (11.32) the index of a basis vector. i j The component x0 is related to the components x by the linear map i i i j x0 = e0 p = e0 e x . (11.33) · · j Such a map from a 4-vector x to a 4-vector x0 is a Lorentz transformation
i i j i i x0 = L x with matrix L = e0 e . (11.34) j j · j i i k The inner product (p, q) of two points p = ei x = ei0 x0 and q = ek y = k ek0 y0 is physical and so is invariant under Lorentz transformations i k i k i k i k (p, q)=x y e e = ⌘ x y = x0 y0 e0 e0 = ⌘ x0 y0 . (11.35) i · k ik i · k ik i i r k k s With x0 = L r x and y0 = L s y , this invariance is r s i r k s i k ⌘rs x y = ⌘ik L r x L s y = ⌘ik x0 y0 (11.36) or since xr and ys are arbitrary
i k i k ⌘rs = ⌘ik L r L s = L r ⌘ik L s. (11.37) In matrix notation, a left index labels a row, and a right index labels a i T i column. Transposition interchanges rows and columns L r = L r ,so
T i k T ⌘rs = L r ⌘ik L s or ⌘ = L ⌘L (11.38) in matrix notation. In such matrix products, the height of an index— whether it is up or down—determines whether it is contravariant or covariant but does not a↵ect its place in its matrix. 11.7 Minkowski Space 457 Example 11.4 (A Boost). The matrix