Foreground separation

Deterministic fitters

The classical fitters in ps_eor.fgfit share a small interface:

from ps_eor import fgfit

fitter = fgfit.PolyForegroundFit(
    deg=3,
    fit_type="power_poly",
)
result = fitter.run(data_cube, noise_cube)

foreground = result.fit
residual = result.sub

The fitted model and residual retain the input cube geometry, so they can be passed directly to flagging or power-spectrum estimation.

PolyForegroundFit

Fits each visibility independently with a polynomial, Bernstein polynomial, power polynomial, or power Bernstein model.

PcaForegroundFit

Separates real and imaginary data using a fixed number of principal components. Its result also exposes the mixing representation and individual reconstructed components.

GmcaForegroundFit

Uses Generalized Morphological Component Analysis, optionally with a frequency transform and polynomial smoothing of the reconstruction.

These methods are useful as inexpensive baselines and when one deterministic subtraction is sufficient. Model order remains a scientific choice: an over-flexible fit can remove signal, while an inflexible fit leaves foreground power in the residual. Use signal injections to quantify that transfer.

ML-GPR

ML-GPR models foreground, systematic, noise, and 21-cm covariances jointly. It returns posterior distributions of component reconstructions and power spectra rather than one fitted subtraction. It also supports multi-epoch models that distinguish shared from observation-dependent structure.

See the ML-GPR user guide for configuration, covariance design, inference, and posterior products. The deterministic and probabilistic interfaces are intentionally different because their result contracts represent different levels of uncertainty.