Sophia Winney | Searcharxiv

arXiv2026

Posterior Predictive Checks for Gravitational-wave Populations: Limitations and Improvements

When selecting a model to characterize an astrophysical population, it is crucial to assess whether that model fits the data and, if not, how it can be improved. To this end, posterior predictive checks (PPCs) are a widely-used statistical test of model fit when inferring gravitational-wave source populations. However, PPCs exhibit limitations when assessing single-event parameters with large measurement uncertainty, like the spin tilt angles of the binary black holes (BBHs) observable with the LIGO-Virgo-KAGRA (LVK) detectors. When single-event inference is prior-dominated, traditional PPCs fail to flag even very poor model fits. In this work, we assess the efficacy of various alternative PPCs on poorly-constrained parameters. We compare PPCs conducted on event- vs. data-level parameters (e.g. posterior samples vs. maximum likelihood points), and explore two additional event-level PPCs: partial predictive checks and split predictive checks. Independent of measurement uncertainty, we find that PPCs on maximum likelihood parameters are always more discerning of model misspecification than any event-level PPC. However, when investigating simulated GWTC-3.0-like catalogs, none of the alternative PPCs show significant improvement over those traditionally used, indicating that at that sensitivity, any limited information in the data about spin tilts is insufficient to diagnose model misspecification. Finally, we apply our suite of PPCs to the spin magnitude and tilt distributions inferred in the most recent LVK catalog, GWTC-4.0. We conclude that the Gaussian Component Spins model used therein under-predicts BBHs with large spin magnitudes and over-predicts those with perfectly anti-aligned tilts.

gr-qc↗

arXiv2024

Effect of the Large Magellanic Cloud on the kinematics of Milky Way satellites and virial mass estimate

We present a study illustrating the effects of the passage of a Large Magellanic Cloud (LMC) mass satellite on the distance and velocity distributions of satellites in $Λ+$Cold Dark Matter simulations of Milky Way (MW) sized halos. In agreement with previous studies, we find that during such a passage the velocity distribution develops a high-velocity tail, which can bias velocity-based virial halo mass estimates. When the velocity distribution of MW satellites is corrected for effects of the LMC passage, it is consistent with the distributions in halos of masses as low as $M_{\rm 200c}=8\times 10^{11}\, M_\odot$ and as high as $1.5\times 10^{12}\,M_\odot$. We present a new halo mass estimator $M_{\rm 200c}=cσ^2_{\rm 3D}r_{\rm med}$, where $c$ is the coefficient calibrated using satellite systems in the simulated MW-sized halos, $σ^2_{\rm 3D}$ is the variance of 3D velocities taken with the sign of the radial velocity of each satellite, and $r_{\rm med}$ is the median halocentric distance of the satellites. We show that the estimator has only $s=8\%$ scatter around the median relation of the estimated and true halo masses and deviates by $<2s$ from the median during the pericentric passage of an LMC-like subhalo. This is because $σ^2_{\rm 3D}$ and $r_{\rm med}$ deviate in the opposite directions during such passages. We apply the estimator to the MW satellite system and estimate the virial mass of the Milky Way of $M_{\rm 200c}=9.96\pm 1.45\times 10^{11}\, M_\odot$, in good agreement with several recent estimates using other methods.

astro-ph.GA↗