How can I Bel-decompose the Riemann tensor over SO(3,3)?

In summary, the conversation is about decomposing the Riemann tensor over SO(3,3) using the Bel decomposition method. There are six parts in the decomposition, each corresponding to different combinations of indices lying in the first or second 3-spaces. The process is similar to the Ricci decomposition in 4-dimensional space, but with the addition of a timelike congruence in 4-dimensions. The paper by Deser provides further insight into the Bel-Robinson tensor and its analogy to the Maxwell energy tensor.
  • #1
deSitter
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Hello,

I need to Bel-decompose the Riemann tensor built over SO(3,3). Does anyone have a decent reference? Funny how this topic is elusive. Is this in Wald?

Thanks in advance.

-drl
 
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  • #2
Not sure what you want, but I'll take a stab at it. You have Rabcd, and you want to split it into parts according to whether the indices lie in the first or second 3-spaces. Bel calls the parts 'electric' and 'magnetic', and SO(3,3) is like Minkowski space except that time is three dimensional. Whatever :-)

Consider first one antisymmetric pair of indices by itself, ab. They can lie in either 11, 12 or 22. That's three cases. Likewise for the second pair, cd. So you have a 3 x 3 matrix of cases, except that Rabcd is symmetric on the two pairs, so the 3 x 3 matrix is symmetrical and there are really only 6 cases: 1111, 1112, 1122, 1212. 1222 and 2222. The decomposition will therefore have six parts.

Is this anywhere near what you want?
 
  • #3
Bill_K said:
Not sure what you want, but I'll take a stab at it. You have Rabcd, and you want to split it into parts according to whether the indices lie in the first or second 3-spaces. Bel calls the parts 'electric' and 'magnetic', and SO(3,3) is like Minkowski space except that time is three dimensional. Whatever :-)

Consider first one antisymmetric pair of indices by itself, ab. They can lie in either 11, 12 or 22. That's three cases. Likewise for the second pair, cd. So you have a 3 x 3 matrix of cases, except that Rabcd is symmetric on the two pairs, so the 3 x 3 matrix is symmetrical and there are really only 6 cases: 1111, 1112, 1122, 1212. 1222 and 2222. The decomposition will therefore have six parts.

Is this anywhere near what you want?

Not really - there is a definite procedure here for carving up the Riemann tensor, sort of like the Ricci decomposition, but I'll be damned if I can remember the details. You need a time-like congruence, which should still exist for SO(3,3) because there is still a light cone. Well I can't remember where I saw this, and need the details. There are actually 4 pieces in general, only 3 survive in 4-d, where they represent tidal distortion and tendency to rotate in the congruence, and the sectional curvatures of the space-like surface orthogonal to the time-like congruence.

edit: Ok I found a paper by Deser talking about it - great! The Bel-Robinson tensor in 4-d is basically sort of like the Maxwell energy tensor in reference to Fmn, that is, you can write Maxwell as

Tmn = Fma Fan + F*ma F*an

and the Bel-Robinson tensor is

Bmnab = Rkmjn Rkajb + R*kmjn R*kajb

where R*mnab = 1/2 epsilon_mnrs Rrsab

I'm speculating that if you think of Rmnab as a thing have two 2-form indices ("surface tensor of 2nd rank" as Pauli called it), then the analogy is exact.

paper by Deser is here

http://arxiv.org/abs/gr-qc/9901007

-drl
 
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  • #4
The timelike congruence Xa used in 4 dimensions is really a projection operator. You need to use two projectors, one projecting on the space dimensions, the other on the time dimension(s). In 4-d, since time is one-dimensional, the projectors can be written in terms of a timelike congruence Xa, namely Pab = XaXb and Qab = nab - XaXb. The Riemann tensor is decomposed by applying one of these projectors to each of the four indices. Taking into account the symmetry of the indices, you wind up with a decomposition into three independent parts.

What I'm saying is that you do likewise in SO(3,3). You still have Pab and Qab, but there's no Xa any longer because 'time' is 3-dimensional. (I suppose if you really insisted on it you could use three congruences and define Pab = XaXb + YaYb + ZaZb.) Project each index onto either the timelike subspace or the spacelike subspace. You'll wind up with six independent parts. For example the first one which I wrote as 1111 earlier would be RabcdPaiPbjPckPdl. The next one would be RabcdPaiPbjPckQdl, and so on.
 
  • #5
Bill_K said:
The timelike congruence Xa used in 4 dimensions is really a projection operator. You need to use two projectors, one projecting on the space dimensions, the other on the time dimension(s). In 4-d, since time is one-dimensional, the projectors can be written in terms of a timelike congruence Xa, namely Pab = XaXb and Qab = nab - XaXb. The Riemann tensor is decomposed by applying one of these projectors to each of the four indices. Taking into account the symmetry of the indices, you wind up with a decomposition into three independent parts.

What I'm saying is that you do likewise in SO(3,3). You still have Pab and Qab, but there's no Xa any longer because 'time' is 3-dimensional. (I suppose if you really insisted on it you could use three congruences and define Pab = XaXb + YaYb + ZaZb.) Project each index onto either the timelike subspace or the spacelike subspace. You'll wind up with six independent parts. For example the first one which I wrote as 1111 earlier would be RabcdPaiPbjPckPdl. The next one would be RabcdPaiPbjPckQdl, and so on.

Good points, thanks for the grist :)

-drl
 

1. What is "Bel decomposition in 6-d"?

"Bel decomposition in 6-d" refers to a mathematical method used in the field of artificial intelligence to represent uncertain or probabilistic information. It involves breaking down a belief function, which assigns probabilities to different outcomes, into multiple components in six dimensions.

2. How is "Bel decomposition in 6-d" used in artificial intelligence?

"Bel decomposition in 6-d" is used to represent uncertain information in artificial intelligence systems, particularly in decision-making processes. It allows for more nuanced and accurate representations of uncertainty, which can lead to better decision-making and predictions.

3. What are the six dimensions used in "Bel decomposition in 6-d"?

The six dimensions used in "Bel decomposition in 6-d" are basic probability assignments (BPAs), commonality, specificity, conflict, commonality specificity interaction, and conflict specificity interaction. These dimensions help break down a belief function into smaller components for a more comprehensive representation of uncertainty.

4. What are the advantages of using "Bel decomposition in 6-d"?

The use of "Bel decomposition in 6-d" allows for a more precise and comprehensive representation of uncertainty. It also helps to address potential conflicts and inconsistencies in the belief function, leading to more accurate decision-making and predictions in artificial intelligence systems.

5. Are there any limitations or challenges associated with "Bel decomposition in 6-d"?

One limitation of "Bel decomposition in 6-d" is the complexity of the method, which may require advanced mathematical and computational skills to implement. Additionally, the accuracy of the results depends on the quality of the initial belief function used. Therefore, it is essential to carefully consider and validate the belief function before applying "Bel decomposition in 6-d".

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