Produce Scalar from Tensor: What's the Name?

In summary: The nondegenerate (i.e. invertible) metric of an n-dimensional Euclidean space has the diagonal form (+1,+1,...,+1,+1) in rectangular coordinates.The nondegenerate (i.e. invertible) metric of an n-dimensional Minkowskian spacetime has the diagonal form (-1,+1,...,+1,+1) in rectangular coordinates.The degenerate (i.e. non-invertible) metrics of an n-dimensional Galilean spacetime has the diagonal forms (0,+1,...,+1,+1) [for the spatial metric]
  • #1
snoopies622
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As I understand it, for a tensor of any rank I can produce a corresponding scalar in the following way: Create an inverted form of the tensor by lowering its upper indices and raising its lower indices, and then taking the inner product of this tensor and the original one.

My only question is, is there a name for this result?
 
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  • #2
Invertibility?
Metric?
Adjointness (or whatever you call "raising/lowering indices", i.e. going from co- to contravariant and vv)
 
  • #3
Loosely speaking, you can call it the "square-norm" since what you are really doing is analogous to [tex] g_{ap} A^a A^p [/tex].
(The quantity [tex] A_a= g_{ap} A^p [/tex] is called the metric-dual of [tex] A^a [/tex].)

Given [tex]A^a{}_b{}^{cd}[/tex], you are forming the scalar using the metric [and its inverse]:
[tex]g_{ap} g^{bq} g_{cr} g_{ds} A^a{}_b{}^{cd} A^p{}_q{}^{rs}[/tex]
 
  • #4
Thanks, robphy.

I was wondering because in another thread someone mentioned using [tex]R^{abcd}R_{abcd}[/tex] (the "Kretschmann scalar"?) in order to show that the event horizon of a Schwarzschild black hole is not a real singularity, and it occurred to me that such a thing might be useful in other circumstances as well, so surely there must be a name for it...

By the way, am I correct in my belief that the "square-norm" of any metric tensor is the number of dimensions of its manifold?
 
  • #5
Yes, you can show that very easily. By definition, [itex]g^{ab} g_{bc} = \delta^a_c[/itex] so if you contract a with c you get [itex]g^{ab} g_{ba} = \sum_{i = 1}^d 1 = d [/itex].
 
  • #6
CompuChip said:
By definition, [itex]g^{ab} g_{bc} = \delta^a_c[/itex] so if you contract a with c you get [itex]g^{ab} g_{ba} = \sum_{i = 1}^d 1 = d [/itex].

Ah, yes, of course. Thanks, CompuChip.
 
  • #7
snoopies622 said:
By the way, am I correct in my belief that the "square-norm" of any metric tensor is the number of dimensions of its manifold?

As seen from CompuChip's response, the metric tensor must have an inverse for that calculation. (Note: The metric of a Galilean spacetime is degenerate.)
 
  • #8
robphy said:
..The metric of a Galilean spacetime is degenerate.

I don't know what this means. Are you referring to a Euclidean metric? Minkowskian? And whether or not their matrix representations have inverses? Don't they?
 
  • #9
snoopies622 said:
I don't know what this means. Are you referring to a Euclidean metric? Minkowskian? And whether or not their matrix representations have inverses? Don't they?

The nondegenerate (i.e. invertible) metric of an n-dimensional Euclidean space has
the diagonal form (+1,+1,...,+1,+1) in rectangular coordinates.

The nondegenerate (i.e. invertible) metric of an n-dimensional Minkowskian spacetime has
the diagonal form (-1,+1,...,+1,+1) in rectangular coordinates.

The degenerate (i.e. non-invertible) metrics of an n-dimensional Galilean spacetime has
the diagonal forms (0,+1,...,+1,+1) [for the spatial metric] and (+1,0,...,0,0) [for the temporal metric] in rectangular coordinates.
 
  • #10
Oh, OK. Thanks.
 

1. What is a scalar?

A scalar is a single numerical value, as opposed to a vector which is a set of multiple values. In mathematics and physics, scalars are used to represent quantities such as time, temperature, and distance.

2. How do you produce a scalar from a tensor?

To produce a scalar from a tensor, you can use the dot product, which multiplies the corresponding elements of two tensors and sums them up to produce a single scalar value. Alternatively, you can also use the trace, which takes the sum of the elements on the main diagonal of a square matrix to produce a scalar.

3. What is the difference between a tensor and a scalar?

A tensor is a mathematical object that can be represented as an array of numbers with multiple dimensions, while a scalar is a single numerical value. Tensors are used to represent higher dimensional data, while scalars are used to represent single quantities.

4. What are some examples of tensors?

Tensors can be found in various fields such as mathematics, physics, and computer science. Some examples of tensors include position vectors, matrices, and images. In physics, tensors are used to represent physical quantities such as force, stress, and strain. In machine learning, tensors are used to store and manipulate data in neural networks.

5. How is a tensor different from a matrix?

While both tensors and matrices are mathematical objects that can be represented as arrays of numbers, they differ in their number of dimensions. A matrix is a two-dimensional array, while a tensor can have any number of dimensions. Additionally, tensors have additional properties and operations that are not applicable to matrices, such as contraction and covariant/contravariant transformations.

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