Scalar Vector Tensor Decomposition

In summary, the perturbed metric can be decomposed into a scalar, vector, and tensor part, with the vector and tensor parts having two degrees of freedom each and the scalar part having one degree of freedom. The vector and tensor parts also have constraints, with the vector being required to be divergence free and the tensor having three constraints due to the contraction of the Kronecker delta and the partial derivative.
  • #1
latentcorpse
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My notes are talking about this scalar vector tensor decopmosition business. Unfortunately, they are not online but they seem to follow this wikipedia article fairly closely:
http://en.wikipedia.org/wiki/Scalar-vector-tensor_decomposition

So our perturbed metric is of the form
[itex]ds^2=a^2(\eta) ( ( 1 + 2 \psi) d \eta^2 - 2 B_i dx^i d \eta - \left( (1-2 \phi) \delta_{ij} + 2 E_{ij} \right) dx^i dx^j)[/itex]
Now apparently the perturbation [itex]E_{ij}[/itex] is symmetric and trace free ([itex]\delta^{ij}E_{ij}=0[/itex])
We can decompose it as follows:
[itex]E_{ij}=\partial_{\langle i} \partial_{j \rangle} E + \partial_{(i} E_{j)}^T + E_{ij}^T[/itex]
where [itex]\partial_{\langle i} \partial_{j \rangle}E=\partial_i \partial_j E - \frac{1}{3} \delta_{ij} \nable^2 E[/itex]

(i) So apparently the first term in the decomposition is the scalar part. Why? It has two indices?

(ii) Again, why is the second term a vector? It also has two indices?

Then he goes on to say that the intial 5 degrees of freedom for [itex]E_{ij}[/itex] (3x3 matrix that's symmetric means 6 d.o.f. and then the constraint of trace free reduces this to 5) get re-distributed as follows:

the scalar gets one degree of freedom.

the vector has two degrees of freedom since it has 3 components and has the constraint that it must be divergence free.

the tensor has two degrees of freedom since [itex]E_{ij}^T[/itex] has five components but [itex]\delta^{ik} \partial_k E_{ij}^T[/itex] is three constraints.

(iii) What is up with the divergence free constraint on the vector bit. He doesn't mention (although he probably meant to) this and I don't see where it has come from?

(iv) I don't understand what [itex]\delta^{ik} \partial_k E_{ij}^T[/itex] is all about or how it leads to three constraints!

Thanks for any help!
 
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  • #2
(i) The scalar part of the decomposition is the first term because it is a scalar quantity. It only has two indices because it is derived from the second-order partial derivatives of the perturbation E_{ij}, which is a second-order tensor. (ii) The second term is a vector because it is derived from the first-order partial derivatives of the perturbation E_{ij}. Since it has three components, it has two degrees of freedom. (iii) The divergence free constraint on the vector bit comes from the fact that the divergence of a vector must be zero for it to be considered divergence free. (iv) \delta^{ik} \partial_k E_{ij}^T is a contraction of the Kronecker delta (\delta^{ik}) and the partial derivative of the tensor part of the perturbation (\partial_k E_{ij}^T). This contraction leads to three constraints because there are three independent components of the partial derivative of the tensor part of the perturbation.
 

Related to Scalar Vector Tensor Decomposition

1. What is Scalar Vector Tensor Decomposition?

Scalar Vector Tensor Decomposition is a mathematical technique used to decompose a multi-dimensional data set into its underlying components. It separates the data into scalar, vector, and tensor components, which can then be analyzed separately.

2. How is Scalar Vector Tensor Decomposition used in science?

Scalar Vector Tensor Decomposition is commonly used in fields such as physics, engineering, and data analysis. It is used to analyze and interpret complex data sets, identify patterns and relationships, and make predictions.

3. What are the benefits of using Scalar Vector Tensor Decomposition?

Using Scalar Vector Tensor Decomposition allows for a better understanding of complex data sets, as it breaks them down into simpler components. It also helps in identifying important features and relationships within the data, leading to more accurate analysis and predictions.

4. What are the limitations of Scalar Vector Tensor Decomposition?

One limitation of Scalar Vector Tensor Decomposition is that it assumes the data set can be accurately represented by scalar, vector, and tensor components. If the data set does not follow this format, the results may not be accurate. Additionally, the interpretation of the components can be subjective and may require prior knowledge of the data.

5. What are some real-world applications of Scalar Vector Tensor Decomposition?

Scalar Vector Tensor Decomposition has many practical applications, including image and signal processing, recommender systems, and pattern recognition. It is also used in fields such as meteorology, finance, and neuroscience for data analysis and prediction purposes.

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