Speaker
Description
Bayesian analyses have long been at the forefront of GW data analyses, mostly using nested sampling. With even state-of-the-art simulation-based-inference methods working in a Bayesian regime, they are here to stay. This has over time lead to discussions about new GW discoveries, be it general-relativity precession, eccentricity, or population-level model comparisons, to be held almost exclusively through the lens of calculating and comparing Bayes Factors. However, these factors are only as valid as the assumptions that allow us to use Bayesian methods in the first place. Currently, none of those assumptions can deal with real detector noise, be it large bias-inducing glitches or just low-SNR non-Gaussianities. This is most evidently shown in the fact that most LIGO-Virgo-KAGRA events with claims of precession, eccentricity, or even lensing, are known to also contain glitches. This talk presents results from a new method using so-called "evidence networks", which statistically determine Bayes Factors based on nothing but the data distribution and can be amortized over real noise. We show that we can re-create nested-sampling results in idealized-noise regimes, and how quickly answers start to diverge in real noise. We plan to use these methods to cross check previous claims and to search for yet-undiscovered effects in LIGO-Virgo-KAGRA data.