Calculating different "kinds" of variations

In summary, the conversation discusses the definition of infinitesimal variations in a Minkowski space, as well as the application of these variations to a relativistic covariant vector wave field. The desired result is to show that for a Poincare transformation with a spin antisymmetric tensor, the variations in the coordinates can be written in terms of the variations in the transformation parameters and the generators of Lorentz transformations. The conversation also includes a link to lecture notes for further reference.
  • #1
Markus Kahn
112
14

Homework Statement


Let ##x## and ##x'## be two points from the Minkowski space connected through a Poincare transformation such that ##x'^\mu =\Lambda_{\nu}^\mu x^\nu+a^\mu## and ##u:\mathcal{M}\to \mathbb{K}=\mathbb{R}## or ##\mathbb{C}##, ##\mathcal{M}## the Minkowski space. We define:
$$
\begin{align*} \Delta u(x) &~:=~ u^{\prime}(x^{\prime})-u(x) \qquad\text{ total infinitesimal variation}\cr \delta u(x) &~:=~ u^{\prime}(x)-u(x) \qquad \text{ local/vertical infinitesimal variation},\cr
\mathrm{d}u(x)&~:=~ u(x^{\prime})- u(x) \qquad\text{ differential/horizontal infinitesimal variation},
\end{align*}
$$
where and ##u'## is the function after the transformation. We further know that we can write
$$\Lambda_\nu^\mu = \delta_\nu^\mu - \frac{i}{2} \delta \omega^{\rho\sigma}(S_{\rho\sigma})^\mu_\nu,$$
with ##\omega^{\mu\nu}+\omega^{\nu\mu}=0##, ##S_{ij}=S_{ij}^\dagger## and ##S_{0k}-S_{0k}^\dagger## (Spin antisymmetric tensor).

Now, show that:
  1. ##\delta x^\mu = \delta \omega^{\mu\nu}g_{\nu\rho}x^\rho +\delta \omega^\mu = \frac{1}{2}\delta\omega^{\rho\sigma}L_{\rho\sigma}x^\mu - i\delta\omega^{\rho}P_\rho x^\mu,## where ##P_\mu = i\partial_\mu## and ##L_{\mu\nu}\equiv x_\mu P_\nu - x_\nu P_\mu.##
  2. For any relativistic covariant vector wave field ##V'_\mu(x')=\Lambda_\nu^\mu V_\nu(x)##, we have ##\Delta V_\mu (x)= -\frac{1}{2}i\delta \omega^{\rho\sigma}(S_{\rho\sigma})_\mu^\nu V_\nu (x).##

Homework Equations


I'm not sure if I really mentioned everything needed to complete the two exercises (since I honestly don't understand how to approach the problem in the first place), so everything that I summerized here can be found in the lec notes of R. Soldati on relativistic quantum field theory (pages 57-63, where I try to explicitly prove the result between eq. 2.16 and 2.17 as well as eq. 2.22), see here: http://robertosoldati.com/archivio/news/107/Campi1.pdf

The Attempt at a Solution


What I've tried:
  1. $$\begin{align*}\delta x^\mu &= x'^\mu-x^\mu = \Lambda_\nu^\mu x^\nu + a^\mu -x^\mu = \left(\delta_\nu^\mu - \frac{i}{2} \delta \omega^{\rho\sigma}(S_{\rho\sigma})^\mu_\nu\right)x^\nu -x^\mu + a^\mu\\ &= \frac{i}{2} \delta \omega^{\rho\sigma}(S_{\rho\sigma})^\mu_\nu x^\nu + a^\mu\ \neq \delta \omega^{\mu\nu}g_{\nu\rho}x^\rho +\delta \omega^\mu. \end{align*}$$ Not sure how I'm supposed to get to the desired result... The last equality is a complete mystery to me. Here I can't even find a way to start calculating what I'm supposed to..
  2. Similarly to above I just plugged in the definition: $$\begin{align*}\Delta V_\mu (x)&= V'_\mu (x') -V_\mu (x)=\Lambda_\mu^\nu V_\nu (x) - V_\mu(x)\\ &= - \frac{i}{2} \delta \omega^{\rho\sigma}(S_{\rho\sigma})^\nu_\mu V_\nu(x), \end{align*}$$ which leads to the desired result. I would still appreciate if someone could give a quick glance over it since I'm rather unsure about the indices...
 
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  • #2
So, can someone please explain me how to approach these kind of problems and how to calculate the desired result? Thanks in advance for any help!
 

1. What are the different kinds of variations that can be calculated?

There are three main types of variations that can be calculated: range, mean deviation, and standard deviation. These measures of variation help to understand the spread or dispersion of a set of data.

2. How is the range calculated?

The range is calculated by subtracting the smallest value in a data set from the largest value. It is a simple measure of variation that gives an idea of how spread out the data is.

3. What is mean deviation?

Mean deviation is a measure of variation that calculates the average difference between each data point and the mean of the data set. It gives an idea of how much the data values deviate from the mean.

4. How is standard deviation different from mean deviation?

Standard deviation is a measure of variation that takes into account the squared differences of each data point from the mean. It is a more precise measure of variation compared to mean deviation, as it gives more weight to extreme values in the data set.

5. Why is it important to calculate variations in data?

Calculating variations in data helps to understand the spread or dispersion of the data set. It can also help to identify outliers or extreme values that may affect the overall interpretation of the data. Variations are also important in statistical analysis and can be used to make comparisons between different data sets.

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