I Need help with tensors and group theory

AI Thread Summary
The discussion revolves around understanding tensors and their representations in the context of group theory, specifically the rotation group SO(3) as presented in A. Zee's "Group Theory in a Nutshell for Physicists." The main confusion arises from why a tensor T, represented as a 9-dimensional object, can be considered a representation of SO(3) despite the traditional view that representations are scalar or square matrices. Participants clarify that while the tensor elements are not independent, they can be decomposed into irreducible representations, indicating that the tensor can indeed furnish a representation. The conversation also touches on the abstract nature of the representation space and mentions Young tableaux as a related concept, although it is not extensively covered in the text. Overall, the discussion highlights the complexities of associating tensors with group representations in physics.
Haorong Wu
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What is the relation between tensors and group SO(3)?
I am reading Group Theory in a Nutshell for Physicists by A. Zee.

I have big problems when learning chapter IV.1 Tensors and Representations of the Rotation Groups SO(N).

It reads
Mentally arrange the nine objects ##T^{ij}## in a column ##
\begin{pmatrix}
T^{11} \\
T^{12} \\
\vdots \\
T^{33}
\end{pmatrix}
##. The linear transformation on the nine objects can then be represetned by a 9-by-9 matrix ##D\left ( R \right )## acting on this column.

For every rotation, specified by a 3-by-3 matrix R, we can thus associate a 9-by-9 matrix ##D\left ( R \right )## transforming the nine objects ##T^{ij}## linearly among themselves.

...

The tensor T furnishes a 9-dimensional representation of the rotation group SO(3).

I can understand why ##D\left ( R \right )## is a representation of SO(3), but I hardly can see why the tensor T can be a representation. I thought a representation should be scalar or square matrix, but why a column consisting of tensors can be a representation, as well.

Also, I do not understand why the tensor came in. If I am correct, elements of groups should be some transformations that leave some objects invariant, but I can hardly imagin how tensors become transformations.

I became more frustrated when the following sections are full of tensors, and I got totally lost.
 
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Maybe these two articles can help:

https://www.physicsforums.com/insights/representations-precision-important/
https://www.physicsforums.com/insights/what-is-a-tensor/

I am not sure why the author calls the nine elements, the coordinates of ##SO(3)## a tensor, but of course we need to consider them as vectors if we construct ##GL(so(3)) ##, which is needed for a (linear) representation. And every vector is automatically a tensor.

So to answer your question, we need to know what you didn't have quoted.
 
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fresh_42 said:
Maybe these two articles can help:

https://www.physicsforums.com/insights/representations-precision-important/
https://www.physicsforums.com/insights/what-is-a-tensor/

I am not sure why the author calls the nine elements, the coordinates of ##SO(3)## a tensor, but of course we need to consider them as vectors if we construct ##GL(so(3)) ##, which is needed for a (linear) representation. And every vector is automatically a tensor.

So to answer your question, we need to know what you didn't have quoted.

Thanks, fresh_42.

The rest content is split into two parts.

First, it explains why ##D\left( R \right )## is a representation of ##SO(3)##, i.e., ##D\left ( R_1 R_2 \right )=D\left( R_1 \right ) D\left( R_2 \right )##, which is obvious.

And the second part explains why the statement, that a tensor ##T^{ij}## transforms as if it were equal to the product of two vectors ##V^i W^j##, is wrong. And this part is not important.

Thus, I skip those two parts.

However, I have found a explanation for myself why the tensor ##T## could furnishes a representation. Here it is.

For every element, that is rotation, in ##SO(3)##, it could be associated with a representation ##D\left( R \right )##. Meanwhile, we can find a tensor ##T##, such that an object in this tensor ##T## must be a linear combination of the nine objects in ##T## under the transformation of ##D\left( R \right )##, so ##T = D(R) \cdot T##. Then I could associate a given ##D\left( R \right )## to this tensor ##T##. Thus, I indirectly associate a rotation with a tensor. Then the tensor ##T## could furnishes a representation of ##SO(3)##.

I am not sure whether this explanation is correct or not.
 
There is a basic problem I have here. If we consider a representation of ##SO(3)##, then we usually speak of a group homomorphism ##SO(3) \longrightarrow GL(V)## with a vector space as representation space ##V##. For a rotation this is normally ##V=\mathbb{R}^3##, the ordinary matrix representation.

Now what are the tensor elements here? ##SO(3)## is no vector space, and neither is ##\operatorname{GL}(V)##. A representation has ##\dim SO(3) \cdot \dim \operatorname{GL}(V)=\dim SO(3)\cdot \dim^2V## many coordinates, which are ##27## in case of ##V=\mathbb{R}^3##, not nine.

To get nine, we consider only one specific element of ##SO(3)##, say the rotation ##R##. Then ##R## maps ##3## coordinates of ##\mathbb{R}^3## on ##3## new coordinates of ##\mathbb{R}^3##, a matrix which we can arrange as a column. But how is it a vector, and a tensor is a vector? What is ##0## in this vector space?
 
fresh_42 said:
There is a basic problem I have here. If we consider a representation of ##SO(3)##, then we usually speak of a group homomorphism ##SO(3) \longrightarrow GL(V)## with a vector space as representation space ##V##. For a rotation this is normally ##V=\mathbb{R}^3##, the ordinary matrix representation.

Now what are the tensor elements here? ##SO(3)## is no vector space, and neither is ##\operatorname{GL}(V)##. A representation has ##\dim SO(3) \cdot \dim \operatorname{GL}(V)=\dim SO(3)\cdot \dim^2V## many coordinates, which are ##27## in case of ##V=\mathbb{R}^3##, not nine.

To get nine, we consider only one specific element of ##SO(3)##, say the rotation ##R##. Then ##R## maps ##3## coordinates of ##\mathbb{R}^3## on ##3## new coordinates of ##\mathbb{R}^3##, a matrix which we can arrange as a column. But how is it a vector, and a tensor is a vector? What is ##0## in this vector space?

Hi, fresh_42. The book I read go through another way.

For a 2-indixed tensor, whose indices can choose from 1 to 3 for SO(3), there would be 9 objects. But, these 9 objects are not all independent. That means the tensor furnishes a reducible representation. We can decompose the 9 objects into a 5-dimensional irreducible representation, a 3-dimensional irreducible representation, and a 1-dimensional irreducible representation.

It seems that the author chose not to represent SO(3) in a linear space but in a abstract tensor space.

I am still confusing, but I am getting to understand it.

Thanks!
 
fresh_42 said:
This sounds as if the author is heading to Young tableaus.

Oh, yes! The Young tableaux is introduced shortly. But the author does not talk a lot about it, just mentions that physicists do not concern it.
 
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