Concept

Principal component analysis — where it appears

A decomposition of a set of observations into the orthogonal patterns that account for most of their variance. It is used to build spectral templates and to model a speckle field, and projecting data onto a few components removes some of whatever signal they contain.

Named by 2 essays across 2 fields — each of them below, with the objects they name alongside it.

Named alongside it

The objects these essays reach for when they reach for this one.

Angular differential imagingContinuum placementContrast curveDetection limitThe Gunn–Peterson troughInjection recoveryThe Lyman-α forestMean transmitted fluxNeutral fractionOptical depthThe point-spread functionQuasar spectrum

All concepts