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Gravitational-wave constraints on are robust to putative redshift evolution in the binary black hole mass spectrum at current sensitivity
Phys. Rev. D 114, 063503 – Published 2 September, 2026
DOI: https://doi.org/10.1103/jk7y-ddyb
Abstract
Spectral-siren cosmology constrains the Hubble constant using gravitational-wave observations of compact-binary coalescences. The method combines luminosity distances inferred from the waveform with redshift information statistically encoded in population features of the source-frame mass spectrum. Because the detector measures redshifted masses, structure in the intrinsic mass distribution acts as an internal “ruler,” making the inference sensitive to assumptions about the population model. In particular, redshift evolution of the mass spectrum is widely discussed as a potential systematic for measurements. We revisit spectral-siren constraints with the GWTC-4.0 binary black hole catalog, explicitly allowing the main mass scales of a standard parametric mass model to evolve with redshift. We find no compelling evidence for evolution at current sensitivity. Allowing evolution produces a modest, nonstatistically significant shift of the posterior toward lower values, which we interpret with targeted posterior and event-level diagnostics. Importantly, the associated systematic uncertainty is subdominant to that induced by alternative redshift-independent descriptions of the mass spectrum, such as the number of spectral features and the functional form used to model them. Our results indicate that, at current sensitivity, spectral-siren constraints on are robust to redshift evolution of the mass spectrum within the flexibility explored here. Using injection studies, we show that this mild shift is reproduced when a nonevolving underlying population is analyzed with an evolving model, consistent with an overflexible population description at the present signal-to-noise. The sign and magnitude of the shift can, however, depend on detector sensitivity and redshift reach as the population features become increasingly constrained directly by the data, motivating targeted diagnostics for future catalogs.
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