How Do You Expand the Antisymmetrized Tensor \( T_{1234} \)?

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In summary, the rule for antisymmetric tensors is to add together all the terms with the same index, with a plus sign in front of components whose indexes are an even permutation, and a minus sign in front of components whose indexes are an odd permutation. Then put a factor of 1/n! in front, where n is the number of indexes.
  • #1
PhyAmateur
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If $$T_{12} = \frac{1}{2} {(T_1 T_2 - T_2 T_1)}$$

That mean antisymmetrization.

How would I expand then $$T_{1234}$$ I find it complicated, it is written on wikipedia for the general case but I can't still deal with these general notations http://en.wikipedia.org/wiki/Antisymmetric_tensor, I mean I can't yet expand it. You would help me if you could expand it or guide me through expanding it using {1,2,3,4} rather than {i,j,k,l} and kronocker delta and those stuff.
 
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  • #2
PhyAmateur said:
If
##T_{12} = \frac{1}{2} {(T_1 T_2 - T_2 T_1)}​

That mean antisymmetrization.

No; antisymmetrization is this:

$$
T_{[12]} = \frac{1}{2} \left( T_{12} - T_{21} \right)
$$

Perhaps that is what you meant to write down; but care with notation is important, particularly if you are trying to expand out more complicated cases.

PhyAmateur said:
How would I expand then
##T_{1234}##​

The general rule is that you add together all the components with the same index, with a plus sign in front of components whose indexes are an even permutation, and a minus sign in front of components whose indexes are an odd permutation. Then you put a factor of 1 / n! in front, where n is the number of indexes.

It should be obvious that what I wrote down for ##T_{[12]}## above (note that I put brackets around the indexes; that is the standard notation for antisymmetrization) satisfies the general rule just given.

For three indexes, we would have

$$
T_{[123]} = \frac{1}{6} \left( T_{123} - T_{132} + T_{231} - T_{213} + T_{312} - T_{321} \right)
$$

You should be able to expand out ##T_{[1234]}## using the general rule as above. Note that there will be 4! = 24 terms. The number of terms is the reason more compact notation for this was invented.
 
  • #3
I linked this answer in the wikipedia link, I was hoping you could help me with the 4 termed one because I can't understand what an odd swap is or an even one?
 
  • #4
Count the number of swaps needed to get there from the start position. Is it odd or even?

Start position is 1234
1243 is odd
2143 is even
etc.
 
  • #5
Swaps of pairs of indices, that is.

Apologies for double post - the edit function is not working on my phone for some reason.
 
  • #6
In general it depends on what you want to antisymmetrize. If you want to antisymmetrize it to all the indices, you have to apply:
[itex] \frac{1}{N!} \epsilon^{abcd} T_{abcd} [/itex].
Where N for 4 indices is 4.

You can check out that this is true for the 2 indices too...
[itex]\frac{1}{2!} \epsilon^{ab} T_{ab} = \frac{1}{2} [ T_{12} - T_{21} ] = T_{[12]}[/itex]

In general antisymmetrization can be seen as using Permutations, and that's the origin of the factor N! ... Because for N indices, you can have N! number of permutations (the number of the elements of the Symmetric Group [itex]S_N[/itex] ).

If you have then to write:
[itex]T_{[12...n]} = \frac{1}{N!} \epsilon^{i_1, i_2, ... , i_n } T_{i_1, i_2 , ... , i_n} [/itex]

In an almost similar way you can work out the antisymmetrization of less than all the indices.

For the 4 then you have a lot, because the symmetric group has 24 elements (so you have 24 terms to put with + or - ...)
 
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  • #7
ChrisVer said:
In general it depends on what you want to antisymmetrize. If you want to antisymmetrize it to all the indices, you have to apply:
[itex] \frac{1}{N!} \epsilon^{abcd} T_{abcd} [/itex].
Where N for 4 indices is 4.

Wouldn't this be a full contraction, and thereby not result in a tensor, but a scalar?
 
  • #8
Matterwave said:
Wouldn't this be a full contraction, and thereby not result in a tensor, but a scalar?

Well the object [itex]T_{12...n} [/itex] is a number, not a tensor.
If you put tensor in the game, like writing : [itex]T_{[ab...m]}[/itex] then on the righthand side you have to put the appropriate Levi-Civita: [itex]T_{[a_1 a_2 ... a_i]} = \frac{1}{(n-i)!} \epsilon_{a_1 a_2 ... a_i b_1 b_2 ... b_{n-i}} \frac{1}{i!} \epsilon^{i_1 i_2 ... i_i b_1 b_2 ... b_{n-i}} T_{i_1 i_2 ... i_i} [/itex]
 

FAQ: How Do You Expand the Antisymmetrized Tensor \( T_{1234} \)?

What is antisymmetrization?

Antisymmetrization is a mathematical operation that involves rearranging elements in a sequence in such a way that if any two elements are swapped, the resulting sequence is the negative of the original sequence. In simpler terms, it is a way of organizing elements in a sequence such that they are not repeated and are always in a specific order.

How is antisymmetrization different from symmetrization?

Antisymmetrization and symmetrization are two different mathematical operations that involve rearranging elements in a sequence. The main difference is that symmetrization results in a sequence that is unchanged when any two elements are swapped, while antisymmetrization results in a sequence that is the negative of the original when any two elements are swapped.

What is the purpose of antisymmetrization in science?

Antisymmetrization is commonly used in science, particularly in quantum mechanics, to describe the behavior of particles that are indistinguishable from each other. By antisymmetrizing the wave function of these particles, we can accurately predict their behavior and interactions.

Can antisymmetrization be applied to non-numerical data?

Yes, antisymmetrization can be applied to non-numerical data, such as in the study of permutation patterns in linguistics and genetics. In these cases, the elements being rearranged are not numbers, but rather words or DNA sequences.

What are some real-world applications of antisymmetrization?

Besides its use in quantum mechanics, antisymmetrization has various applications in fields such as chemistry, biology, and computer science. It is used to describe and predict the behavior of particles, molecules, and genetic sequences. It is also used in data analysis and pattern recognition algorithms.

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