Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

<jats:p>We present a harmonic-space estimator for the cross-correlation between the kinematic Sunyaev-Zel’dovich (kSZ) effect and the reconstructed galaxy momentum field that offers several practical advantages over the traditional stacking approach. The estimator is easy to deploy using relatively modest computational resources and recovers all information available in the galaxy-kSZ cross-correlation. In particular, by using well-understood power spectrum estimation techniques, its statistical uncertainties, including potential correlated uncertainties with other large-scale structure observables, can be easily and accurately estimated. Moreover, standard kSZ stacking measurements can be reconstructed exactly from the estimator at a lower computational cost, employing harmonic-space, catalog-level techniques to recover all small-scale information.</jats:p>

Abstract:

Abstract:

Abstract Statistical methodology is rarely considered significant in distance-ladder studies or a potential contributor to the Hubble tension. We suggest it should be, highlighting two appreciable issues. First, astronomical distances are inferred latent parameters, requiring a prior. We show that the (often implicit) uniform priors on distance moduli common to Bayesian distance-ladder analyses bias distances low due to objects being uniformly distributed in volume, which biases the Hubble constant high. Frequentist χ2 methods are unbiased for volume- or redshift-limited samples only if the redshift uncertainty (including peculiar velocities) vanishes, though simulation-based calibration can correct the bias. Second, in a Bayesian framework, selection effects introduce additional posterior factors describing the probability of objects entering the sample under the model. These partly counteract the volume prior, depending on the nature of the selection. After detailed analytic and mock-based studies, we quantify the volume-prior effect in the CosmicFlows-4 and SH0ES samples. Both use frequentist methods, so the effect appears as a potential estimator bias rather than a missing prior. The implied Hubble constant shifts are significant but must not be applied naïvely—principled selection modelling is also required, as we investigate explicitly for CosmicFlows-4. Both effects should already be captured by the SH0ES pipeline’s simulation-based bias corrections. Our work highlights the crucial need to model both distances and selection accurately, either directly in a Bayesian forward model, or via post-hoc simulation-based corrections with realistic source and selection distributions. Such modelling requires samples with known, homogeneous selection criteria, which future surveys should prioritise.