Orateur
Description
The numerous sources that LISA will observe pose a unique data analysis challenge, with the most promising current avenue being a Global Fit, a simultaneous inference of all sources. Within the LISA Global fit, stochastic sources, from instrumental noise, to the galactic foreground and astrophysical or cosmological backgrounds, are particularly relevant due to their effect on the quality of the inference of all other deterministic sources. Issues such as non-gaussianity, due to poor subtraction of deterministic signals, or data gaps, scheduled and unscheduled, provide a unique opportunity for machine learning to augment traditional Bayesian methods. We present a stochastic signal inference framework for LISA data analysis, based on simulation-based inference, with a state-of-the-art all-in-one framework, the Simformer. We further augment the inference network with a novel physics-aware embedding scheme. This framework can recover the individual stochastic components, including spline-based and per-link noise degrees of freedom, in the presence of realistic glitch rates and antenna pattern-repointing-driven gaps or a supermassive black hole binary. Furthermore, we provide a comprehensive testing suite of our network, both at a population and per-event level.