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注意峰值:利用半参数质量模型改进GWTC-4.0谱警报的宇宙学限制

Mind the peak: improving cosmological constraints from GWTC-4.0 spectral sirens using semiparametric mass models

Università di Bologna(博洛尼亚大学);INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna(博洛尼亚天文物理与空间科学研究所);INFN - Sezione di Bologna(博洛尼亚意大利国家核物理研究所)

Matteo Tagliazucchi, Michele Moresco, Nicola Borghi, Chiara Ciapetti

AI总结:

本文采用半参数方法分析GWTC-4.0中的137个双黑洞事件,通过自适应放置knots来捕捉质量分布的三个峰值,提升了H0的精度,提供了新的宇宙学约束。

摘要:

引力波谱警报可通过二元黑洞(BBH)质量分布(MD)的形状提供宇宙学限制。然而,这些限制的精度和准确性取决于能否捕捉所有MD特征。本文分析了GWTC-4.0中的137个BBH事件,采用基于B-spline的新型数据驱动半参数方法,自适应地在MD最信息丰富的结构周围放置knots,同时保持参数空间的维度适度。我们的灵活模型解析出三个不同的峰值,分别位于约10、18和33个太阳质量处,并在统计上优于标准参数模型,贝叶斯因子高达226。由于这些特征与H0相关,半参数模型在不同先验假设下,相对于参数模型提供了12%-21%的H0精度提升,最佳情况下给出H0 = 57.8^{+21.9}_{-20.6} km/s/Mpc。我们的结果表明,捕捉BBH质量分布的全部复杂性对于实现谱警报的宇宙学潜力至关重要,随着引力波目录的持续增长,这一点尤为关键。

英文摘要:

Gravitational wave spectral sirens can provide cosmological constraints by using the shape of the binary black hole (BBH) mass distribution (MD). However, the precision and accuracy of these constraints depends critically on the capturing all the MD features. In this work, we analyze 137 BBH events from the latest GWTC-4.0 with a novel data-driven semiparametric approach based on \textsc{Bspline} that adaptively places knots around the most informative structures in the MD, while keeping the dimensionality of the parameter space moderate. Our flexible models resolve three distinct peaks at $\sim10$, $18$, and $33\,\mathrm{M}_\odot$ and are statistically preferred over standard parametric models, with Bayes factors up to 226. Because these features are correlated with $H_0$, the semiparametric model yields, under different prior assumptions, 12%-21% improvement in the precision of $H_0$ relative to parametric models, providing $H_0 = 57.8^{+21.9}_{-20.6}\,\mathrm{km/s/Mpc}$ in the best case. Our results demonstrate that capturing the full complexity of the BBH mass distribution is essential for realizing the cosmological potential of spectral sirens as gravitational wave catalogs continue to grow.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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