Obtaining the Metric in a Boosted Observer Frame?

In summary, the conversation discusses obtaining a metric in the frame of a boosted observer in a spacetime with coframe and frame basis vectors. The new frame basis is obtained by boosting the coordinate frame in the phi direction. There is some uncertainty in reading off the new coframe basis, but the resulting metric seems plausible. The conversation also mentions the concept of a local frame and its associated dual coframe, which are defined as smooth vector fields and 1-forms, respectively, satisfying certain conditions.
  • #1
Mentz114
5,432
292
I'm trying to get a metric in the frame of a boosted observer. The spacetime in question has coframe and frame basis vectors

[tex]
\begin{align*}
\vec{\sigma}^0 = \frac{-1}{\sqrt{F}}dt\ \ \ \ & \vec{e}_0 = -\sqrt{F}\partial_t \\
\vec{\sigma}^1 = \sqrt{F}dz\ \ \ \ & \vec{e}_1 = \frac{1}{\sqrt{F}}\partial_z \\
\vec{\sigma}^2 = \sqrt{F}dr\ \ \ \ & \vec{e}_2 = \frac{1}{\sqrt{F}}\partial_r \\
\vec{\sigma}^3 = r\sqrt{F}d\phi\ \ \ \ & \vec{e}_3 = \frac{1}{r\sqrt{F}}\partial_\phi
\end{align*}
[/tex]

Boosting the coordinate frame basis by [itex]\beta[/itex] in the [itex]\phi[/itex] direction gives the new frame basis

[tex]
\begin{align*}
\vec{f}_0 &= -\gamma\sqrt{F}\partial_t + \gamma\beta \frac{1}{r\sqrt{F}}\partial_\phi \\
\vec{f}_1 &= \frac{1}{\sqrt{F}}\partial_z \\
\vec{f}_2 &= \frac{1}{\sqrt{F}}\partial_r \\
\vec{f}_3 &= \gamma\frac{1}{r\sqrt{F}}\partial_\phi + \gamma\beta \sqrt{F}\partial_t
\end{align*}
[/tex]

Now, my problem is reading off the new coframe basis [itex]s[/itex]. My attempt is below, but I'm only 50% confident it's right.

[tex]
\begin{align*}
{\vec{s}}^0 &= (\gamma\sqrt{F})^{-1}dt+(\gamma\beta)^{-1}r\sqrt{F}d\phi \\
{\vec{s}}^1 &= \sqrt{F}dz \\
{\vec{s}}^2 &= \sqrt{F}dr \\
{\vec{s}}^3 &= \gamma^{-1}r\sqrt{F}d\phi + (\gamma\beta)^{-1}\sqrt{F}dt
\end{align*}
[/tex]

The metric that arises from this is sort of plausible. I'd appreciate any pointers, particularly to any errors.
 
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  • #2
From Lee's book "Riemanian Manifolds : An Introduction to Curvature" ( page 30)

Let (E1, . . . , En) be any local frame for TM, that is, n smooth vector fields defined on some open set U such that (E1|p, . . . , En|p) form a basis for TpM at each point p ∈ U. Associated with such a frame is the dual coframe, which we denote (ϕ1, . . . , ϕn); these are smooth 1-forms satisfying ϕi(Ej) = δij.

Couldn't be simpler really.
 

What is "Boosting the frame basis"?

"Boosting the frame basis" is a technique used in signal processing and data analysis to improve the accuracy and efficiency of analyzing data. It involves transforming the data into a new basis or set of coordinates, which allows for easier identification and extraction of important features.

How does "Boosting the frame basis" work?

The process of "boosting the frame basis" involves finding the optimal set of coordinates or basis in which the data can be represented more accurately. This is achieved by iteratively updating the basis and re-evaluating the data until the desired level of accuracy is achieved.

What are the benefits of "Boosting the frame basis"?

"Boosting the frame basis" can improve the accuracy and efficiency of data analysis by reducing noise, enhancing important features, and making the data easier to interpret. It can also help to identify hidden patterns and relationships within the data that may not be apparent in the original basis.

What types of data can benefit from "Boosting the frame basis"?

"Boosting the frame basis" can be applied to various types of data, including time series data, images, and audio signals. It is particularly useful for data with complex and nonlinear relationships, where traditional methods may not be as effective.

Are there any limitations to "Boosting the frame basis"?

While "boosting the frame basis" can be a powerful tool for data analysis, it may not be suitable for all types of data. It requires a good understanding of the data and may require significant computational resources. Additionally, the quality of the results may depend on the specific problem and the chosen basis.

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