Connections and Ricci identity

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In summary, the Homework Equations state that-R^a_{[bcd]} = 0 = 3!R^a_{\,\,bcd} + R^a_{\,\,cdb} + R^a_{\,\,dbc} - R^a_{\,\,bdc} - R^a_{\,\,cbd} - R^a_{\,\,dcb}-R^d_{\,\,cab}e_d.
  • #1
CAF123
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1. Homework Statement
Given ##\nabla## a torsionless connection, the Ricci identity for co-vectors is $$\nabla_a \nabla_b \lambda_c - \nabla_b \nabla_a \lambda_c = -R^d_{\,\,cab}\lambda_d.$$
Prove ##R^a_{[bcd]} = 0## by considering the co-vector field ##\lambda_c = \nabla_c f##

Homework Equations


$$R^a_{[bcd]} = 0 = \frac{1}{3!} \left(R^a_{\,\,bcd} + R^a_{\,\,cdb} + R^a_{\,\,dbc} - R^a_{\,\,bdc} - R^a_{\,\,cbd} - R^a_{\,\,dcb}\right)$$

The Attempt at a Solution


Input the given form for the covector into the Ricci identity in the question. Then since ##\nabla_c f = e_c(f),## we have
$$\nabla_a \nabla_b e_c(f) - \nabla_b \nabla_a e_c(f) = -R^d_{\,\,cab}e_d(f).$$ True for all functions f, so $$\nabla_a \nabla_b e_c - \nabla_b \nabla_a e_c = -R^d_{\,\,cab}e_d.$$ Then since ##\nabla_a e_b = \Gamma^d_{ba} e_d## we can simplify the above to give $$\nabla_a \Gamma^d_{cb}e_d - \nabla_b \Gamma^d_{ca}e_d = -R^d_{\,\,cab}e_d$$ which can then be further rewritten like $$\nabla_a \Gamma^d_{cb} + \Gamma^{\alpha}_{cb}\Gamma^d_{\alpha a} - \nabla_b \Gamma^d_{ca} - \Gamma^{\alpha}_{ca}\Gamma^d_{\alpha b} = -R^d_{\,\,cab}.$$ I was then going to relabel all indices to get terms like that in the equation in 'Relevant Equations' and sum them all up and I hoped to get zero, but it is not. Have I made an error in the above somewhere? Thanks!
 
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  • #2
I'm a bit rusty on this stuff, but,... since no one else has replied,...

Where did this problem come from? Is it from a textbook? Online notes? (If the latter, please provide a link.)

CAF123 said:
$$R^a_{[bcd]} = 0 = \frac{1}{3!} \left(R^a_{\,\,bcd} + R^a_{\,\,cdb} + R^a_{\,\,dbc} - R^a_{\,\,bdc} - R^a_{\,\,cbd} - R^a_{\,\,dcb}\right)$$
Since ##R^a_{bcd}## is already skewsymmetric in the last 2 indices, you can simplify the rhs down to 3 terms, which is a sum of cyclically permuted b,c,d, indices. In that form it's called the "first Bianchi identity". Proving that might be less work. Check out the associated formulas on Wikipedia.

Also, I don't understand how you went from your 2nd-last line to your last line.
 
  • #3
I just realized... the correct method is sketched in Wald p39.

(I guess I'm rustier than I realized.)
 
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1. What is the Ricci identity?

The Ricci identity is an equation in differential geometry that relates the curvature of a manifold to the Lie derivative of a tensor field. It is often used to study the properties of connections on a manifold.

2. How does the Ricci identity relate to connections?

The Ricci identity is closely related to connections, as it describes how the curvature of a manifold changes under parallel transport along a curve. This is important because connections allow us to define a notion of parallelism on a manifold, which is crucial in understanding the geometry of the manifold.

3. What is the significance of the Ricci identity in general relativity?

In general relativity, the Ricci identity is used to study the properties of spacetime and how it is affected by the presence of matter and energy. It is a key equation in Einstein's field equations, which describe the relationship between the curvature of spacetime and the distribution of matter and energy.

4. Can the Ricci identity be used to determine the curvature of a manifold?

Yes, the Ricci identity is often used in conjunction with other equations, such as the Gauss-Codazzi equations and the Bianchi identities, to determine the full curvature of a manifold. These equations allow us to calculate the Riemann curvature tensor, which fully characterizes the curvature of a manifold.

5. Are there any applications of the Ricci identity outside of differential geometry?

Yes, the Ricci identity has applications in other fields such as physics, computer science, and statistics. In physics, it is used to study the properties of black holes and other objects with strong gravitational fields. In computer science, it is used in algorithms for image and shape recognition. In statistics, it is used in the analysis of data with non-Euclidean structures.

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