Raising and Lowering Indices of the Metric

In summary, using coordinate basis vectors and the relationship between basis one forms and coordinate basis vectors, you can express a vector in terms of its coordinate basis vectors.
  • #1
Shmi
12
0

Homework Statement



"Evaluate: [itex] g^{\mu \nu} g_{\nu \rho}[/itex] where [itex] ds^2 = g_{\mu \nu} dx^\mu dx^\nu , ds^2 = c^2 dt^2 - dx^2 - dy^2 - dz^2 [/itex]"

Homework Equations



None necessary, just a notation issue

The Attempt at a Solution



Just using raising and lowering rules, I imagine each raising and lowering the others non-summed index or "contracting" the dummy index as I've heard it called.

[tex] g^{\mu \nu} g_{\nu \rho} = g_\mu^\rho [/tex]

But that's a bit easy.

In matrix form, this new object [itex]g_\mu ^\rho[/itex] is a four by four identity matrix when you take the product of the two matrices.

This leads to me to think that [itex] g^{\mu \nu} g_{\nu \rho} = g_\mu^\rho = \delta _\mu ^\rho[/itex] is some intrinsic identity of this combination of metric tensors, and a quick google search does indeed reveal that to be the case.

What I'm really struggling with is that most sites present this as an obvious consequence of a matrix and its inverse. I can dig a matrix and it's inverse resulting in an identity, but I don't see how these matrices are inverses. Furthermore, this action produces a (1,1) tensor. Of course, given the manipulation rules, I see why it is a (1,1) but I don't see why me make the distinction for an identity matrix, or if it's just a matter of consistency.

If you could shed some insight on what's going as we raise and lower indices, I'd love to see why this result is so obvious to the authors of other literature on index notation.
 
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  • #2
Shmi said:

Homework Statement



"Evaluate: [itex] g^{\mu \nu} g_{\nu \rho}[/itex] where [itex] ds^2 = g_{\mu \nu} dx^\mu dx^\nu , ds^2 = c^2 dt^2 - dx^2 - dy^2 - dz^2 [/itex]"

Homework Equations



None necessary, just a notation issue

The Attempt at a Solution



Just using raising and lowering rules, I imagine each raising and lowering the others non-summed index or "contracting" the dummy index as I've heard it called.

[tex] g^{\mu \nu} g_{\nu \rho} = g_\mu^\rho [/tex]

But that's a bit easy.

In matrix form, this new object [itex]g_\mu ^\rho[/itex] is a four by four identity matrix when you take the product of the two matrices.

This leads to me to think that [itex] g^{\mu \nu} g_{\nu \rho} = g_\mu^\rho = \delta _\mu ^\rho[/itex] is some intrinsic identity of this combination of metric tensors, and a quick google search does indeed reveal that to be the case.

What I'm really struggling with is that most sites present this as an obvious consequence of a matrix and its inverse. I can dig a matrix and it's inverse resulting in an identity, but I don't see how these matrices are inverses. Furthermore, this action produces a (1,1) tensor. Of course, given the manipulation rules, I see why it is a (1,1) but I don't see why me make the distinction for an identity matrix, or if it's just a matter of consistency.

If you could shed some insight on what's going as we raise and lower indices, I'd love to see why this result is so obvious to the authors of other literature on index notation.

The g with the upper indices is DEFINED to be the inverse of the g with lower indices. That's really all there is to it. For other tensors the raising and lowering goes through the metric tensor.
 
Last edited:
  • #3
Another simple way of deriving your result is to make use of the coordinate basis vectors [itex]\vec{a_i}[/itex] and the basis one forms [itex]\vec{a^j}[/itex]. These are related by [tex]\vec{a_i}\centerdot \vec{a^j}=\delta_i^j[/tex]. If we use this to express the coordinate basis vectors in terms of the basis one forms, we get:
[tex]\vec{a_i}=g_{ij}\vec{a^j}[/tex] where

[tex]g_{ij}=\vec{a_i}\centerdot\vec{a_j}[/tex]

If we now dot this equation with [itex]\vec{a^k}[/itex], we get:

[tex]\delta_i^k=g_{ij}g^{jk}[/tex]

which is your desired relationship.
 

Related to Raising and Lowering Indices of the Metric

1. What is meant by "raising and lowering indices of the metric"?

"Raising and lowering indices of the metric" refers to the mathematical process of changing the position of indices (subscripts and superscripts) in the metric tensor. This process is used in general relativity to transform between different representations of the same tensor, such as from covariant to contravariant or vice versa.

2. Why do we need to raise and lower indices of the metric?

In general relativity, tensors with different index positions have different physical interpretations and can behave differently under coordinate transformations. By raising or lowering indices, we can convert between these different representations and make the tensor easier to work with in a particular situation.

3. How is an index raised or lowered in the metric?

An index is raised or lowered in the metric by using the metric tensor to contract it with another index. For example, to raise an index, we multiply it by the metric tensor and sum over all possible values of the index. To lower an index, we multiply the tensor by the inverse of the metric tensor and sum over all possible values of the index.

4. What is the significance of the metric tensor in raising and lowering indices?

The metric tensor is a fundamental object in general relativity that encodes the geometric properties of spacetime. It is used to define the distance between two points and to raise and lower indices of tensors. Without the metric tensor, it would not be possible to perform these operations and describe the curvature of spacetime.

5. Are there any practical applications of raising and lowering indices in physics?

Yes, raising and lowering indices of the metric are essential for making calculations and predictions in general relativity. It is also used in other areas of physics, such as quantum field theory, where tensors with different index positions can represent different physical quantities. Additionally, the process of raising and lowering indices is used in computer simulations and numerical calculations that model complex physical systems.

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