Dimensional reduction retains fields independent of chosen internal coordinates. In a flat-torus zero-mode reduction, a metric supplies a metric, internal-component vectors and internal metric scalars; a massless p-form gauge field supplies lower-rank fields with internal indices. Fluxes, twists or projections can alter the spectrum and preserved supercharges.
The zero-mode dimensional reduction on a flat -torus splits a massless p-form gauge field by the number of spacetime indices. Each sector has copies and local polarizations per copy. Comparing coefficients in gives , proving polarization matching. Four-dimensional three- and four-form potentials contribute no local propagating states.
// Target: supersymmetry.bigb
Articles by others on the same topic
Dimensional reduction is a process used in data analysis and machine learning to reduce the number of random variables or features in a dataset while preserving its essential information. This is particularly useful when dealing with high-dimensional data, which can be challenging to visualize, analyze, and model due to the "curse of dimensionality" — a phenomenon where the feature space becomes increasingly sparse and less manageable as the number of dimensions increases.