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Laboratories

A useful computation answers a question you can state before running it. These laboratories connect executable calculations to their analytic lessons: identify the inputs, reproduce a benchmark, then change one assumption and explain the result. Each page includes the defining equations, interpretable output, complete solutions, and editable downloads with their package requirements.

You can read the calculations without running a notebook service or browser simulation. Choose a lab that follows the theory you already know; the preparation links below lead to the necessary derivations.

  • Gamma matrices and bilinears. Begin with three-dimensional spinors and Fierz identities. Test the Clifford algebra, then rearrange products of commuting and anticommuting spinors. Changing a gamma-matrix sign tests orientation; changing the entry statistics tests a different assumption.
  • Chiral multiplet closure. Follow off-shell closure through ordered Grassmann products. Verify the transformation algebra on every component, then remove the auxiliary field and identify the precise equation-of-motion residue. More successful matrix identities cannot compensate for a wrong parity convention.

Vacuum identifications and effective couplings

Section titled “Vacuum identifications and effective couplings”

For each experiment, first predict what the changed input should do. Then compare with a check that could reveal a different error: an analytic limit, a separate representation, or a calculation with different numerical failure modes. Several wrappers around one formula do not provide independent confirmation.

Exact arithmetic checks the algebra that was represented. It does not prove that those equations describe the intended theory. Numerical precision, integration resolution, domain cutoffs and physical approximations must likewise be tested separately. A small rounding error cannot repair an incorrect contour, missing exchange term or wrong normalization.

You have reached the lab’s exit calculation when you can reproduce its benchmark, account for the changed-input result and state the limits of the conclusion. Use the learning paths to continue the physical argument.