Tensor Density Transformation Law: Order of Jacobian Matrix?

In summary: There are two common conventions, and both are used in the literature. However, most authors seem to prefer the Carroll convention (the first one in the OP), so it may be considered the "standard" one.In summary, the question is regarding the order of the numerator and denominator in the Jacobian matrix that multiplies the expression for tensor transformation. The two sources, Sean M. Carroll's Lecture Notes on General Relativity and Wikipedia, have different conventions for the Jacobian matrix. However, both conventions are equivalent and depend on the weight of the relative tensor being transformed. There is a discussion about this issue on the Wikipedia talk page, and the Carroll convention is generally considered to be the "standard" one.
  • #1
binbagsss
1,259
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I just have a quick question on which order around the numerator and denominator should be in the jacobian matrix that multiplies the expression.

As in general Lecture Notes on General Relativity by Sean M. Carroll, 1997 he has the law as

##
\xi_{\mu'_{1}\mu'_{2}...\mu'_{n}}=|\frac{\partial x^{\mu'}}{\partial x^{\mu}} | \frac{\partial x^{\mu_{1}}}{\partial x^{\mu'_{1}}}\frac{\partial x^{\mu_{2}}}{\partial x^{\mu'_{2}}}...\frac{\partial x^{\mu_{n}}}{\partial x^{\mu'_{n}}} \xi_{\mu_{1}\mu_{2}...\mu_{n}}##

whereas on wiki the law is

##\xi^{\alpha}_{beta}= |[\frac{\partial \bar{x}^{t}}{\partial x^{\gamma}}]|\frac{\partial x^{\alpha}}{\partial \bar{x}^{\delta}}\frac{\partial \bar{x}^{\epsilon}}{\partial x^{\beta}}\bar{\xi}^{\delta}_{\epsilon}##

So both sources seem to have the matrix the other way around relative to the 'orginal' tensor and what is being transformed.

Does the order not matter?
Thanks.
 
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  • #2
It looks to me both your definitions are equivalent. How the Jacobian appears will also depend on the weight of the relative tensor.
 
  • #3
Orodruin said:
It looks to me both your definitions are equivalent.
How? The first has the partial derivative of the transformed coordinates with respect to the partial derivative of the ' origninal' coordinates.
The second has the partial derivative of the orginial coordinates with respect to the partial derivative of the 'transformed' coordinates.
Orodruin said:
How the Jacobian appears will also depend on the weight of the relative tensor.
Whilst I see the second is transforming a (1,1) tensor density and the first a (n,0) tensor density, the first therefore lies in the same subset of the second so would expect the transformation law , coming from the lower indices, to take the same form.
 
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  • #4
binbagsss said:
How? The first has the partial derivative of the transformed coordinates with respect to the partial derivative of the ' origninal' coordinates.
The second has the partial derivative of the orginial coordinates with respect to the partial derivative of the 'transformed' coordinates.

This has to do with the contravariant/covariant indices in the tensor density that you are describing, not with the Jacobian.

binbagsss said:
Whilst I see the second is transforming a (1,1) tensor density and the first a (n,0) tensor density, the first therefore lies in the same subset of the second so would expect the transformation law , coming from the lower indices, to take the same form.

If you lower the ##\alpha## of the second transformation law using the metric (or construct a new tensor density with only lower indices by contraction with any rank two covariant tensor), the transformation laws are equivalent. Being a relative tensor does not have to do with how the individual indices transform, but how the entire tensor transforms apart from the transformations imposed by the location of the indices.
 
  • #5
Orodruin said:
This has to do with the contravariant/covariant indices in the tensor density that you are describing, not with the Jacobian.

.


Sorry?My comment above and the OP is referring to the Jacobian only and not the other matrices, I understand the other matrices ok I think.
 
  • #6
I see, I missed the subtlety in that you have used ##x## as the original coordinates in one of the definitions and as the transformed coordinates in the other.

Still, it is only a difference in how the weight of the relative tensor is defined. One definition is the negative of the other.
 
  • #7
In fact, there is a (quite heated) discussion about this very issue on the Wikipedia talk page. The TLDR of that is basically that it is all conventional.
 
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Related to Tensor Density Transformation Law: Order of Jacobian Matrix?

1. What is the Tensor Density Transformation Law?

The Tensor Density Transformation Law is a mathematical concept that describes how a tensor density changes under a coordinate transformation. A tensor density is a mathematical object that combines the properties of a tensor (a multilinear mapping) and a density (a scalar function that assigns a weight to each point in space). It is used to represent physical quantities that may vary in different directions and at different rates.

2. What is the Order of a Jacobian Matrix?

The order of a Jacobian matrix refers to the number of variables in a coordinate transformation. It is equal to the number of rows and columns in the Jacobian matrix. For example, a 2D coordinate transformation would have an order of 2, while a 3D coordinate transformation would have an order of 3.

3. How is the Tensor Density Transformation Law related to the Jacobian Matrix?

The Tensor Density Transformation Law is directly related to the Jacobian matrix. The Jacobian matrix is a matrix of partial derivatives that describes how the coordinates of a point change when transformed from one coordinate system to another. The Tensor Density Transformation Law uses the Jacobian matrix to describe how a tensor density transforms under the same coordinate transformation.

4. What is the importance of the Tensor Density Transformation Law in scientific research?

The Tensor Density Transformation Law is essential in scientific research because it allows for the accurate representation of physical quantities that vary in different directions and at different rates. It is commonly used in fields such as fluid dynamics, electromagnetism, and general relativity to describe the behavior of physical systems and make predictions.

5. Are there any applications of the Tensor Density Transformation Law outside of science?

Yes, the Tensor Density Transformation Law has applications in mathematics, engineering, and computer science. It is used to solve problems related to vector calculus, differential equations, and geometric transformations. In computer science, it is used in image processing, computer graphics, and machine learning algorithms.

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