Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 87 tok/s
Gemini 2.5 Pro 51 tok/s Pro
GPT-5 Medium 17 tok/s Pro
GPT-5 High 23 tok/s Pro
GPT-4o 102 tok/s Pro
Kimi K2 166 tok/s Pro
GPT OSS 120B 436 tok/s Pro
Claude Sonnet 4 37 tok/s Pro
2000 character limit reached

Spectral siren cosmology from gravitational-wave observations in GWTC-4.0 (2509.03607v1)

Published 3 Sep 2025 in astro-ph.CO and astro-ph.HE

Abstract: Gravitational wave standard sirens offer a promising avenue for cosmological inference, particularly in measuring the expansion history of the universe. Traditionally, bright sirens require an electromagnetic counterpart to determine the redshift of the emission source while dark sirens rely on the presence of complete galaxy catalogs over large sky regions. Spectral sirens, using GW data alone, can circumvent these limitations by leveraging features in the mass distribution of compact binaries. With the recent release of the Gravitational-Wave Transient Catalog 4 (GWTC-4.0), the number of significant binary black hole (BBH) merger candidates has increased to 153, enabling more robust population studies and cosmological constraints. This work builds upon previous spectral siren analyses by analyzing the latest BBH observations with parametric and non-parametric models. In particular, we consider a parametric approach using the Powerlaw + Peak and Broken Powerlaw + 2 Peaks models as well as a more flexible non-parametric model based on Gaussian processes. We find broad consistency in the inferred Hubble constant $H_0$ constraints across models. Our most constraining result is from the Gaussian Process model, which, combined with the GW170817 bright siren measurement, results in $H_0 = 69{+7}_{-6} \ \mathrm{km\,s{-1}\,Mpc{-1}}$, a 10% precision measurement. For the Powerlaw + Peak and Broken Powerlaw + 2 Peaks we find fractional uncertainties of 17% and 13% respectively.

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube