A Foundation for Gravitational-Wave Population Inference within the LISA Global Fit

TL;DR

This paper introduces a new statistical framework and a GPU-accelerated tool for population inference in LISA gravitational-wave data, addressing the complex interplay of resolved and unresolved sources.

Contribution

It develops a formalism for joint inference of sources and populations within the LISA global fit, and presents PELARGIR, a prototype software for this purpose.

Findings

Demonstrated the formalism with a toy model analysis.

Developed PELARGIR, a GPU-accelerated population inference module.

Outlined a roadmap for implementing this approach in real LISA data analysis.

Abstract

Population inference in gravitational-wave astronomy allows us to connect individual detections to the astrophysics of compact objects and their environments. Current approaches employed for population inference with LIGO-Virgo-KAGRA data approximate evaluation of the hierarchical population likelihood via post-processing of individual-event posteriors. However, the case of the Laser Interferometer Space Antenna (LISA) will be more complex for two main reasons: the transdimensional "global fit" approach to LISA data analysis which models all signals and noise simultaneously, and the presence of both individually-resolved signals and the unresolved stochastic ``Galactic foreground" arising from the Galactic binary population, which induces a circular dependence between the resolved and unresolved systems and our ability to detect the former. These challenges are not without opportunity; LISA's data will contain every mHz compact binary in the Milky Way -- either individually or within the Galactic foreground -- with great potential for Galactic and stellar astrophysics. We therefore propose an alternative approach: direct evaluation of the full hierarchical population likelihood within the LISA global fit. We develop a statistical formalism for joint inference of individually-resolved gravitational-wave sources, an unresolved stochastic foreground, and a shared, underlying astrophysical population, present PELARGIR, a prototype GPU-accelerated population inference module for the LISA global fit, demonstrate the formalism and PELARGIR via a toy model analysis, and lay out a roadmap towards an astrophysically-motivated LISA global fit with embedded population inference. While we apply the formalism here to the population of LISA Galactic binaries, it is applicable across the gravitational-wave spectrum with use cases in pulsar timing and next-generation terrestrial observatories.