Correlated Galactic Confusion Foreground for the LISA-Taiji-TianQin Network and Its Implications for Resolvable Source Analysis
Abstract: The millihertz gravitational-wave sky is expected to be observed by a network of space-based detectors (LISA, Taiji, and TianQin) in the 2030s. A dominant noise in this band is the confusion foreground produced by O(107) unresolved Galactic binaries (GBs). Because the same population projects onto the time-delay interferometry (TDI) channels of the different detectors, the foreground is necessarily correlated across the network. We construct the full foreground noise covariance matrix for the LISA-Taiji-TianQin network from a catalogue of ~3 x 107 GBs through numerical simulation, and we derive an analytic model of the cross-detector foreground coherence that provides a cross-check and physical interpretation of the numerical results. We derive the overall sensitivity of the detector network based on this covariance matrix, and further characterize the frequency- and time-dependent cross-detector foreground correlations. Taking massive black hole binaries (MBHBs) and GBs as representative transient and continuous sources, we further compare the block-diagonal (i.e., neglecting the cross-detector foreground correlation) and full-covariance noise models in terms of their impacts on the signal-to-noise ratio (SNR), parameter uncertainties, and Bayesian posteriors. The impact is confined to specific signal regimes, affecting primarily high-mass (M_c ~ 107 M_sun) MBHBs and low-frequency (f_0 <~ 2 mHz) GBs, with SNR relative differences of up to ~30% and ~10%, respectively. No statistically significant parameter bias arises under either noise model, as verified by probability-probability tests. Parameter estimations for MBHBs and GBs are performed using the network analysis pipelines implemented in the Triangle-BBH and Triangle-GB codes. Both repositories, together with the simulated foreground data and network sensitivities, are publicly released for diverse scientific investigations.
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