PETRA: From the LISA global fit to a catalog of Galactic binaries

TL;DR

This paper introduces a method to extract a reliable catalog of Galactic binaries from LISA's complex global-fit data, addressing label-switching ambiguities and providing source probabilities.

Contribution

It presents a novel post-processing algorithm that relabels global-fit samples to produce a consistent astrophysical catalog, applicable to any global-fit LISA data output.

Findings

Successfully applied to toy models and simulated LISA data

Produces source posterior distributions and origin probabilities

Implemented as an open-source Python package

Abstract

The Laser Interferometer Space Antenna (LISA) will detect mHz gravitational waves from many astrophysical sources, including millions of compact binaries in the Galaxy, thousands of which may be individually resolvable. The large number of signals overlapping in the LISA dataset requires a \emph{global fit} in which an unknown number of sources are modeled simultaneously. This introduces a \emph{label-switching ambiguity} for sources in the same class, making it challenging to distill a traditional astronomical catalog from global-fit posteriors. We present a method to construct a catalog by post-processing the global-fit posterior, relabeling samples to minimize the statistical divergence between the global fit and a factorized catalog representation. The resulting catalog consists of the source posterior distributions and their probabilities of having an astrophysical origin. We demonstrate our algorithm on two toy models and on a small simulated LISA dataset of Galactic binaries. Our method is implemented in the open-source Python package \textsc{petra\_catalogs}, and it can be applied in postprocessing to the output of any global-fit sampler.