Residual Galactic binary foreground in LISA stochastic gravitational-wave background inference: source power concentration and spectral degeneracy
Abstract
Galactic compact binaries are expected to form a dominant foreground in the millihertz band of the Laser Interferometer Space Antenna (LISA). Residual power from injected sources that do not meet the adopted recovery criteria can bias stochastic gravitational-wave background (SGWB) inference or increase its uncertainty. We use LISA Data Challenge 2A Sangria injections and the Erebor comparison table to construct a catalog residual spectrum between 0.4 and 6.0 mHz with orbit-averaged long-wavelength Michelson source powers. The source power concentration in each frequency bin determines the excess kurtosis of a random-phase source sum; instrumental noise and fiducial SGWB power strongly reduce the resulting excess kurtosis in most bins. The residual spectrum also overlaps an isotropic power-law SGWB in the mean binned power. We use a fixed covariance obtained by summing independent Fourier-mode power variances. For a frequency-independent SGWB with fiducial amplitude , marginalizing over the dimensionless residual-power factor increases the uncertainty by when the residual power is distributed uniformly over the Fourier frequencies in each bin. The largest Gaussian prior standard deviation on that limits this increase to is . More concentrated distributions of the residual power among Fourier frequencies reduce the increase, reflecting unresolved frequency structure. Omitting the fiducial residual with the covariance held fixed shifts the best-fitting by times the uncertainty obtained with fixed. This projection of the residual spectrum onto the SGWB spectrum is not a posterior detection significance. The numerical values are conditional on the catalog-level scalar power model, fixed instrumental noise, and independent mode-power covariance.
Keywords
Cite
@article{arxiv.2607.25349,
title = {Residual Galactic binary foreground in LISA stochastic gravitational-wave background inference: source power concentration and spectral degeneracy},
author = {Ruo-Yu Guan and Yan Wang},
journal= {arXiv preprint arXiv:2607.25349},
year = {2026}
}
Comments
16 pages, 6 figures, and 2 tables