Solution to the dirac equation and the square root of a matrix?

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Discussion Overview

The discussion revolves around the solutions to the Dirac equation, specifically focusing on the interpretation of negative frequency solutions and the mathematical concept of taking the square root of a matrix within this context.

Discussion Character

  • Technical explanation
  • Conceptual clarification
  • Debate/contested

Main Points Raised

  • One participant presents a form of the solution to the Dirac equation involving a two-component spinor and questions the interpretation of taking the square root of a matrix in this context.
  • Another participant explains the concept of the square root of a matrix, noting that there can be multiple square roots and discussing the diagonalization process to find them.
  • A participant inquires about the source of the expression mentioned, asking if it was from Peskin & Schroeder or another reference.
  • A later reply indicates that the expression was found in David Tong's lecture notes, providing a link to the source.

Areas of Agreement / Disagreement

Participants do not appear to reach a consensus on the interpretation of the square root of a matrix in the context of the Dirac equation, and there are multiple references mentioned without agreement on a single source.

Contextual Notes

The discussion includes assumptions about the properties of matrices and their square roots, as well as the specific context of solutions to the Dirac equation, which may not be fully resolved.

center o bass
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Hi. I'm currently reading about (negative frequency) solutions to the Dirac equations which can be written on the form

[tex]\Psi = ( \sqrt{p \cdot \sigma} \chi, \sqrt{p \cdot \bar{\sigma}} \chi)^T e^{-i p \cdot x}[/tex]For any two component spinor Chi. But the dot product with the four vector p and the sigma vector is a matrix, so here one is taking the square root of a matrix. What do we mean by that? Or am I interpreting this wrong?
 
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The square root of a matrix A is another matrix B such that BB = A. Note that, as with the square roots of numbers, there is more than one square root of a matrix. Off the top of my head, I think that a general NxN matrix could have up to 2^N distinct square roots.

If you can diagonalize A, so that A = M^-1 D M, where D is diagonal, then D lists the eigenvalues of A. Then you can verify that M^-1 sqrt(D) M is a square root of A, where sqrt(D) is a diagonal matrix whose entries are the square roots of the entries of D. So the square root of a matrix A has the same eigenvectors as A, but with eigenvalues that are the square roots of the eigenvalues of A.

Are you reading Peskin & Schroeder? I seem to recall that they make a comment to the effect that by convention they take the positive square root. So when they take the square roots of the eigenvalues, they choose positive signs.
 
center o bass, Did you see this expression in Peskin & Schroeder, or somewhere else?
 

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