How to work out the tetrads for given functions?

  • I
  • Thread starter etotheipi
  • Start date
In summary, the author has defined a tetrad that is dependent on two functions, f and h, which are functions of the coordinate r. The author also explains how to work out the normal derivative operator associated with these coordinates for each of the tetrad vectors. The solution is obtained using the chain rule and the fact that the vectors dx_mu are constant, resulting in a simplified expression.
  • #1
etotheipi
Given functions ##f=f(r)## and ##h = h(r)##, author has defined a tetrad$$(e_0)_a = f^{1/2} (dt)_a, \quad (e_1)_a = h^{1/2}(dr)_a, \quad (e_2)_a = r(d\theta)_a, \quad (e_3)_a = r\sin{\theta} (d\phi)_a$$where ##(t,r,\theta,\phi)## are coordinates. It is required to work out ##\partial_{[a} (e_\mu)_{b]}## for each of these, with ##\partial_a## the normal derivative operator associated with these coordinates. So e.g. for the first one I would have thought$$
\begin{align*}
\partial_{[a} (e_0)_{b]} = \partial_{[a} f^{1/2} (dt)_{b]} = \frac{1}{2}f^{-1/2} (\partial_{[a} f) (dt)_{b]} + f^{1/2} \partial_{[a} (dt)_{b]} \ \ \ (1)
\end{align*}
$$I figured that ##\partial_{\mu} f = \delta_{\mu r} f'##, i.e. zero for ##\mu = t,\theta,\phi## and ##f'## for ##\mu = r##. But solution is$$\partial_{[a} (e_0)_{b]} = \frac{1}{2} f^{-1/2} f' (dr)_{[a} (dt)_{b]} \ \ \ (2)$$How to get from ##(1)## to ##(2)##? Thanks
 
  • Like
Likes Dale
Physics news on Phys.org
  • #2
etotheipi said:
author

Are you referring to a particular textbook or paper? If so, please give a reference.
 
  • Like
Likes etotheipi
  • #3
PeterDonis said:
Are you referring to a particular textbook or paper? If so, please give a reference.

it's chapter 6.1, page 121 of wald
 
  • #4
etotheipi said:
it's chapter 6.1, page 121 of wald

Ah, ok, I'll take a look.
 
  • Love
Likes etotheipi
  • #5
etotheipi said:
I figured that ##\partial_{\mu} f = \delta_{\mu r} f'##

Remember that Wald's Latin indexes are abstract indexes, i.e., ##\partial_a## is not ##\partial_r## in this case, it's a set of four derivative operators, which in this case are the coordinate derivative operators: ##\partial_t##, ##\partial_r##, ##\partial_\theta##, ##\partial_\phi##. We happen to know that, for the function we are interested in, which is a scalar function of ##r## only, three of these four derivative operators give zero; but Wald is deliberately staying with abstract index notation to make clear what is happening, geometrically: we are taking the derivative of a scalar and the result is a vector.

Now, in what direction does that vector point, when we take the derivative of a scalar function of ##r## only? Obviously, it points in the direction of increasing ##r##. So, if we were just taking the derivative of ##f(r)## by itself, we would have ##\partial_a f(r) = f^\prime(r) \left( dr \right)_a##. The rest is just the chain rule and the extra factor of ##\left( dt \right)_b##.
 
  • Like
Likes etotheipi
  • #6
Ah, I think the ##\partial_a f = f' (dr)_a## part makes sense now. But also, how can we show that the other term ##f^{1/2} \partial_{[a} (dt)_{b]}## vanishes?
 
  • #7
etotheipi said:
how can we show that the other term ##f^{1/2} \partial_{[a} (dt)_{b]}## vanishes?

All of the vectors ##\left ( dx^\mu \right)_a## are constants, since they're just the coordinate basis vectors. The variation in the tetrads is contained entirely in the functions that multiply them. So all quantities of the form ##\partial_{[a} \left( dx^\mu \right)_{b]}## vanish.
 
  • Like
Likes etotheipi
  • #8
PeterDonis said:
All of the vectors ##\left ( dx^\mu \right)_a## are constants, since they're just the coordinate basis vectors. The variation in the tetrads is contained entirely in the functions that multiply them. So all quantities of the form ##\partial_{[a} \left( dx^\mu \right)_{b]}## vanish.

Nice! Thanks for your help ☺
 
  • #9
etotheipi said:
Thanks for your help ☺

You're welcome!
 
  • Love
Likes etotheipi
  • #10
PeterDonis said:
##\partial_a## is not ##\partial_r## in this case, it's a set of four derivative operators, which in this case are the coordinate derivative operators: ##\partial_t##, ##\partial_r##, ##\partial_\theta##, ##\partial_\phi##.

And to be strictly correct and capture the "which direction does the vector point" issue, we should really write this as:

$$
\partial_a = \left( dt \right)_a \partial_t + \left( dr \right)_a \partial_r + \left( d \theta \right)_a \partial_\theta + \left( d \phi \right)_a \partial_\phi
$$
 
  • Like
Likes etotheipi

FAQ: How to work out the tetrads for given functions?

What are tetrads in relation to functions?

Tetrads refer to a set of four elements or variables that are used to describe a function. These elements are typically denoted as a, b, c, and d and represent the coefficients of a quadratic function in the form of ax^2 + bx + c = d.

How do I determine the tetrads for a given function?

To determine the tetrads for a given function, you need to first identify the coefficients of the function. These coefficients will correspond to the elements a, b, c, and d in the tetrad. Once you have identified these coefficients, you can then plug them into the tetrad formula, which is (a, b, c, d) = (a, b, c, d).

What is the purpose of working out tetrads for a function?

The purpose of working out tetrads for a function is to have a standardized way of representing and analyzing quadratic functions. Tetrads allow us to easily identify key elements of a function, such as the vertex, axis of symmetry, and roots.

Can tetrads be used for functions other than quadratics?

Yes, tetrads can be used for other types of functions as well, such as cubic or quartic functions. However, the tetrad formula may vary depending on the type of function.

Are tetrads used in real-world applications?

Yes, tetrads are commonly used in fields such as physics, engineering, and economics to model and analyze real-world phenomena. They can be used to predict the behavior of systems and make informed decisions based on mathematical models.

Similar threads

Replies
1
Views
649
Replies
16
Views
3K
Replies
3
Views
3K
Replies
11
Views
967
Replies
4
Views
571
Replies
2
Views
2K
Replies
5
Views
817
Back
Top