Majorana representation of Gamma matrices.

In summary, there are two different expressions for Majorana representation of the gamma matrices. One paper uses real matrices while the Wikipedia article shows all complex matrices. It is unclear which is the actual representation or if both are valid. The number of possible representations is not limited and it is up to the individual to choose which basis to use. However, the gamma matrices are only defined up to similarity transformations and have caused stress and mistakes in calculations in the past.
  • #1
arroy_0205
129
0
It is well known that at times we do need explicit representations for the Dirac gamma matrices while doing calculations with fermions. Recently I found two different expressions for Majorana representation for the gamma matrices. In one paper, the form used is:
[tex]
\gamma_{0} = \left(
\begin{array}{cc}
0 & i\sigma_2\\
i\sigma_2 & 0
\end{array}
\right)
[/tex]
[tex]
\gamma_{1} = \left(
\begin{array}{cc}
\sigma_1 & 0\\
0 & \sigma_1
\end{array}
\right)
[/tex]
[tex]
\gamma_{2} = \left(
\begin{array}{cc}
0 & -i\sigma_2\\
i\sigma_2 & 0
\end{array}
\right)
[/tex]
[tex]
\gamma_{3} = \left(
\begin{array}{cc}
\sigma_3 & 0\\
0 & \sigma_3
\end{array}
\right)
[/tex]
[tex]
\gamma_{5} = \left(
\begin{array}{cc}
\sigma_2 & 0\\
0 & \sigma_2
\end{array}
\right)
[/tex]
However in wikipedia article on gamma matrices, the Majorana representations are diffenrent and all are complex matrices. See: http://en.wikipedia.org/wiki/Dirac_matrices#Majorana_basis
I am confused which is the actual representation of Majorana representation? Or are both representations valid Majorana representations? Note that in the rep. I wrote, the first four matrices are real matrices.

Also can anybody tell me how to write several matrices side-by-side in latex?
Thanks.
 
Last edited:
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  • #2
Is there any definite rule to obtain gamma matrix representations? Or can I use my own representations if I find the matrices satisfy the anticommutation relations? Is there any limit on the number of possible representations of gamma matrices in a given dimension?
 
  • #3
gamma matrices are only defined up to similarity transformations; which representation or basis you wish to use is up to you.
 
  • #4
They're also amongst the leading causes of stress disorders amongst physicists.

The number of times a factor of i, or -1 from a mismatch of conventions with them, has bungled a calculation at this point is a matter of historical importance.
 

1. What is the Majorana representation of Gamma matrices?

The Majorana representation of Gamma matrices is a specific way of writing the gamma matrices in terms of real numbers instead of complex numbers. This representation was first introduced by Italian physicist Ettore Majorana in the 1930s.

2. Why is the Majorana representation important?

The Majorana representation is important because it simplifies calculations involving gamma matrices, which are used to describe the spin of particles in quantum field theory. It also has applications in condensed matter physics and quantum computing.

3. How is the Majorana representation different from other representations of Gamma matrices?

The Majorana representation differs from other representations in that it uses only real numbers, while other representations may involve complex numbers. Additionally, the Majorana representation is unique and can only be used for a specific number of dimensions (4, 8, or 16).

4. How is the Majorana representation related to the Dirac representation?

The Majorana representation is closely related to the Dirac representation, as both involve writing the gamma matrices in terms of real numbers. The Majorana representation can be obtained from the Dirac representation by taking the gamma matrices to be purely imaginary and applying a specific transformation.

5. What are some applications of the Majorana representation?

The Majorana representation has applications in various fields of physics, including quantum field theory, condensed matter physics, and quantum computing. It has been used to study topological insulators and superconductors, as well as for constructing quantum gates in quantum computing algorithms.

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