Representation decomposition

This allows us to decompose 5 and 10 into representations of SO(4), as 5 → 4 ⊕ 1 and 10 → 6 ⊕ 4.
  • #1
Augbrah
3
0

Homework Statement



Show that vector representation 5 and adjoint representation 10 in SO(5) decompose respectively into representations of SO(4) as:

541
10
64

Homework Equations

The Attempt at a Solution


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I understand that 5 is rep of SO(5) corresponding to Dynkin labels (1, 0). 1 is of course trivial representation. But what type of rep is 4? Once I started calculating weights I found out that Dynkin labels corresponding to (2, 0) as well as (1, 1) for SO(4) both have 4 weights (and different ones). And none of them seem to reproduce 5! Maybe there is an easier way, how would you show these decomposition?

Weights for 5 [Dynkin (1,0) for SO(5)]:
(1, 0), (-1, 2), (0,0), (1, -2) (0, -1)

Weights for 4? [Dynkin (1,1) for SO(4)]:
(1,1), (-1, 1), (1, -1), (-1, -1)

Weights for 4? [Dynkin (3, 0) for SO(4)]:
(3, 0), (1, 0), (-1, 0), (-3, 0)
 
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  • #2


To show this decomposition, we can use the branching rules for SO(5) and SO(4). The branching rule for SO(5) → SO(4) is given by:

(1, 0) → (2, 0) ⊕ (1, 1)

This means that the representation (1, 0) of SO(5) can be decomposed into a representation (2, 0) and a representation (1, 1) of SO(4).

Using this rule, we can see that the representation (1, 0) of SO(5) decomposes into a representation (2, 0) of SO(4) and a trivial representation (1, 0) of SO(4). This is the same as the decomposition shown in the forum post, 5 → 4 ⊕ 1.

Similarly, the branching rule for SO(5) → SO(4) can be used to show that the representation (2, 0) of SO(5) decomposes into a representation (1, 1) of SO(4) and a representation (1, 0) of SO(4). This gives us the decomposition 10 → 6 ⊕ 4, as shown in the forum post.

In summary, to show these decompositions, we need to use the branching rules for SO(5) → SO(4) and the fact that the representation (1, 0) of SO(5) corresponds to the vector representation, while the representation (2, 0) corresponds to the adjoint representation.
 

1. What is representation decomposition?

Representation decomposition is a process used in data analysis and machine learning to break down a complex data set into simpler, more manageable components called representations. These representations are then used to better understand the data and make predictions or classifications.

2. How does representation decomposition work?

Representation decomposition starts by identifying patterns or features in a data set. These patterns are then used to create a set of representations, which are smaller, simpler data sets that capture the most important information from the original data. This process can be done using various techniques such as principal component analysis, independent component analysis, and non-negative matrix factorization.

3. What are the benefits of using representation decomposition?

Representation decomposition can provide several benefits, including reducing the complexity of a data set, identifying hidden patterns and relationships, and improving the performance of machine learning algorithms. It can also help with data visualization, making it easier to interpret and communicate the results of a data analysis.

4. What are some real-world applications of representation decomposition?

Representation decomposition has many practical applications in fields such as image and speech recognition, natural language processing, and recommendation systems. It is also commonly used in data compression, signal processing, and feature selection for machine learning models.

5. Are there any limitations to representation decomposition?

While representation decomposition can be a powerful tool, it does have some limitations. It relies on the assumption that the data can be broken down into simpler components, which may not always be the case. It also requires careful consideration of which decomposition technique is most appropriate for a particular data set and may not always result in a perfect representation of the original data.

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