LISA's Stochastic Foreground Reveals Galactic Binary Population

This research presents a novel approach to extracting information about the galactic binary population from the stochastic gravitational-wave foreground detected by LISA. The method employs a simulation-based inference framework, bypassing the computational challenges associated with traditional hierarchical methods. By adopting an astrophysically agnostic parametrization in the observable space, the researchers generate synthetic catalogs and foreground spectra. A neural posterior estimator is then trained to map these spectra to population parameters. The validation of the method on simulated data demonstrates its ability to recover population parameters with good accuracy, including the total number of binaries. The development of a GPU-accelerated version of the subtraction algorithm represents a significant advancement, providing a ~100X speed-up compared to previous implementations. This speed-up is crucial for handling the large datasets generated by LISA. The results suggest that LISA's stochastic foreground alone carries significant information about the Galactic binary population and provide a practical step toward joint inference from resolved and unresolved sources. Further research is needed to refine the simulation models and address potential biases. The success of this approach could significantly enhance our understanding of galactic evolution and the distribution of binary star systems.

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