Extend the joint spline framework to realistic LISA data complexities

Extend the Bayesian joint spline framework for LISA instrumental-noise and stochastic gravitational-wave-background inference to handle time-varying arm lengths, data gaps, and non-stationary noise, potentially using a time-frequency treatment.

Background

The study evaluates the joint log-P-spline framework under an idealized LISA configuration with equal arm lengths, stationary noise, and perfectly known response and transfer functions. These assumptions simplify inference but omit operational and instrumental effects expected in realistic LISA data.

The authors explicitly leave the extension to time-varying arm lengths, data gaps, and non-stationary noise for future investigations, noting that such complexities will likely require a time-frequency formulation rather than the stationary frequency-domain treatment used in the paper.