TAPIR Seminar

In person: 370 Cahill. To Join via Zoom: 851 0756 7442

Abstract: Gravitational-wave (GW) observations of black-hole binaries provide access to both the properties of individual events and the statistical behavior of their populations. In this talk, I present two complementary approaches to GW data analysis that adopt distinct physical assumptions and methodologies.

First, I discuss a recent analysis of the ringdown signal from the event GW190521 using waveform models that incorporate spin-induced precession. Within a neural-network-based inference framework, I show that modeling precession in the progenitor binary yields modest yet systematic shifts in the recovered binary parameters compared with non-precessing analyses.

I then turn to ongoing work on population-level inference of black-hole binaries formulated at past-time infinity. Using synthetic catalogs, this approach addresses the challenge of consistently incorporating orbital eccentricity in this limit. I outline the current limitations of inference in this regime, and a possible resolution based on the role of orbital angular momentum.