Orthogonality of inner product of generators

In summary, the inner product between generators of a Lie algebra is commonly defined as \mathrm{Tr}[T^a T^b]=k \delta^{ab} . This trace is orthogonal because it is a choice that is made for convenience. The generators form an orthogonal basis for the vector space that is the Lie algebra. This choice allows for easy derivation of things like the invariant measure for integrations over the group. Additionally, all semi-simple compact Lie groups have a "natural" metric structure and can be identified using the Cartan catalogue. For more information, refer to Weinberg's Quantum Theory of Fields, Vol. 2.
  • #1
PineApple2
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0
Hi, this is a rather mathematical question. The inner product between generators of a Lie algebra is commonly defined as [itex] \mathrm{Tr}[T^a T^b]=k \delta^{ab} [/itex]. However, I don't understand why this trace is orthogonal, i.e. why the trace of a multiplication of two different generators is always zero.
 
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  • #2
This is a choice we make for convenience. The generators form a basis for vector space that is the Lie algebra, and it is convenient to choose an orthogonal basis. We can always choose a basis that is orthogonal.
 
  • #3
The only thing you should worry about is whether the trace tr(AB), where A and B are elements of the Lie algebra, is an inner product or not. If you have an inner product, you can always select an orthogonal basis.
 
  • #4
This is the great thing with semi-simple compact Lie groups. Their generators can always be chosen such as they are "orthogonal" in the sense you wrote. This implies that the Lie group, as a differentiable manifold (with the group operations providing differentiable mappings), has a "natural" metric (Riemann-space) structure and you can thus easily derive things like the invariant measure for integrations over the group (Haar measure), using Weyl's unitarity trick (proving that all finite-dimensional representations are equivalent to a unitary one) etc. Last but not least, all semi-simple compact Lie groups are identified ("Cartan catalogue"). For details, see Weinberg, Quantum Theory of Fields, Vol. 2.
 
  • #5
I see, that makes sense. Thank you all for answering
 

1. What is the concept of orthogonality in the inner product of generators?

The concept of orthogonality in the inner product of generators refers to the property of two generators being perpendicular to each other. This means that their inner product is equal to zero, indicating that they are independent and do not share any common components. In other words, they are orthogonal to each other.

2. Why is orthogonality important in the inner product of generators?

Orthogonality is important in the inner product of generators because it allows for a more efficient representation of a vector space. It helps to reduce redundancy and simplifies calculations by decomposing the vector space into independent subspaces. In addition, it allows for the use of orthogonal bases, which are easier to work with in linear algebra.

3. How is orthogonality of inner product of generators related to linear independence?

Orthogonality of inner product of generators is closely related to linear independence. Two vectors that are orthogonal to each other are also linearly independent. This means that one vector cannot be expressed as a linear combination of the other, making them independent and essential in spanning the vector space.

4. Can the inner product of generators be non-orthogonal?

Yes, the inner product of generators can be non-orthogonal. This happens when two generators are not perpendicular to each other and have a non-zero inner product. In this case, the vectors are dependent and share common components, making them less efficient for representing a vector space.

5. How is orthogonality of inner product of generators used in practical applications?

Orthogonality of inner product of generators has a wide range of applications in various fields, including signal processing, image processing, and quantum mechanics. In signal processing, it is used for data compression, noise reduction, and feature extraction. In image processing, it is used for image enhancement and object detection. In quantum mechanics, it is used for quantum state preparation and quantum information processing.

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