Calculating the Invariant Matrix

In summary, the effective Lagrangian for a process involving the weak interaction can be expressed in terms of Dirac spinors, u and v. When computing the matrix element, the appropriate spinors for each particle must be used, as shown in the example of muon decay. This is also true for QED scatterings.
  • #1
ChrisVer
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I am having one question... If we know the form of the effective Lagrangian, let's say the form:

[itex]L= g (\bar{\psi}_{e} \gamma^{\mu} P_{L} \psi_{\nu})(\bar{\psi}_{p} \gamma_{\mu} P_{L} \psi_{n}) [/itex]

How can someone calculate the spin averaged invariant matrix [itex]\large M[/itex]?
I mean I can do the whole calculation if it's to have the [itex]u,v[/itex] in place of [itex]\psi[/itex]. I am having also a problem of seeing when this is done in QED scatterings too, since we know that [itex]M= j^{\mu}j_{\mu}[/itex] with [itex]j^{\mu}= \bar{\psi} \gamma^{\mu} \psi [/itex]...
but I don't know if I have the whole Dirac spinor [itex]\psi[/itex] what someone is supposed to do?

In most cases for the weak interaction, I'm seeing [itex]M[/itex] given by [itex]u,v[/itex] (like in Halzen & Martin)...

Thanks
 
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  • #2
It's exactly the calculation you are used to in terms of the u and v spinors. What appears in the Lagrangian (for example the Fermi Lagrangian that you wrote) is the Dirac field which can be expressed as a sum of Dirac spinors, u and v.

So suppose you want to describe the effective muon decay, [itex]\mu^-\to e^-+ \nu_\mu +\bar\nu_e[/itex]. Then your effective Lagrangian is going to be:
$$
L=\frac{G_F}{\sqrt{2}}\left(\bar\psi_\mu\gamma^\alpha P_L\psi_{\nu_\mu}\right)\left(\bar \psi_{\nu_e}\gamma_\alpha P_L\psi_e\right).
$$
However, when computing the matrix element using the Feynman diagram this Lagrangian leads to:
$$
\mathcal{M}=\frac{G_F}{\sqrt{2}}\left(\bar \nu_\mu\gamma_\alpha P_L\mu\right)\left(\bar e\gamma^\alpha P_L\nu_e\right).
$$
Now in the matrix element you have the spinors appropriate for each particle and thus you can use all the well-known trace technology to compute the square amplitude averaged over the spins.
 
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Related to Calculating the Invariant Matrix

1. What is an invariant matrix?

An invariant matrix is a square matrix that remains unchanged under a transformation, such as rotation or reflection. This means that the matrix has the same values before and after the transformation.

2. How do you calculate the invariant matrix?

The invariant matrix can be calculated by finding the eigenvalues and eigenvectors of the original matrix. The eigenvectors are then used to create a similarity matrix, which is multiplied by the original matrix to obtain the invariant matrix.

3. Why is the invariant matrix important?

The invariant matrix is important because it allows us to identify patterns and relationships between different sets of data. It also helps us to simplify complex calculations and make predictions based on the properties of the matrix.

4. What are some applications of the invariant matrix?

The invariant matrix has various applications in mathematics, physics, and computer science. It is used in image and signal processing, data analysis, and machine learning algorithms. It is also used in the study of symmetry and symmetry breaking in physical systems.

5. Can the invariant matrix be calculated for any matrix?

Yes, the invariant matrix can be calculated for any square matrix. However, the resulting matrix may not always have useful properties or applications. It is more commonly used for matrices that represent real-world data or systems with underlying symmetry.

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