Bayesian Forecasts on Cosmic Superstring Searches with LISA

Abstract

Cosmic superstrings are well-motivated early-Universe sources of stochastic gravitational waves, with phenomenology controlled by the string tension $G\mu$ and the intercommutation probability $P$. We study whether LISA can reconstruct these parameters and distinguish different superstring signal models in the presence of instrumental noise and astrophysical foregrounds. We consider two phenomenological models. In Model I, reduced intercommutation acts only as an amplitude enhancement of a cusp-dominated spectrum, $\Omega_{\rm SS}^{\rm I}=P^{-\beta}\Omega_{\rm cusp}$. In Model II, the signal is a cusp--kink mixture, $\Omega_{\rm SS}^{\rm II} =P^{-\beta}[p_c\Omega_{\rm cusp}+(1-p_c)\Omega_{\rm kink}]$, so that $P$ controls both amplitude and spectral shape. Using simulated LISA data, we perform Bayesian inference with noise uncertainties, unresolved extragalactic compact-binary backgrounds, and a flexible Galactic double-white-dwarf foreground. We compare the models using Bayesian evidences and map the $(G\mu,P)$ posterior geometry with marginalized widths, correlations, covariance anisotropy, principal eigenvalues, posterior area, and bias. We find that reduced intercommutation generically produces strong $G\mu$--$P$ correlations, because the data often constrain an amplitude-like combination. However, when the cusp--kink spectral difference lies in the LISA band and the signal is sufficiently loud, Model II can be favored and the posterior can retain genuine shape information. As an optimistic foreground scenario, we also compute the Bayes factor using a reduced tanh Galactic foreground template, finding improved model discrimination when the foreground shape is constrained.