Suppr超能文献

Spectral Sirens: Cosmology from the Full Mass Distribution of Compact Binaries.

作者信息

Ezquiaga Jose María, Holz Daniel E

机构信息

Kavli Institute for Cosmological Physics and Enrico Fermi Institute, The University of Chicago, Chicago, Illinois 60637, USA.

Department of Physics, Department of Astronomy and Astrophysics, The University of Chicago, Chicago, Illinois 60637, USA.

出版信息

Phys Rev Lett. 2022 Aug 5;129(6):061102. doi: 10.1103/PhysRevLett.129.061102.

Abstract

We explore the use of the mass spectrum of neutron stars and black holes in gravitational-wave compact binary sources as a cosmological probe. These standard siren sources provide direct measurements of luminosity distance. In addition, features in the mass distribution, such as mass gaps or peaks, will redshift and thus provide independent constraints on their redshift distribution. We argue that the entire mass spectrum should be utilized to provide cosmological constraints. For example, we find that the mass spectrum of LIGO-Virgo-KAGRA events introduces at least five independent mass "features": the upper and lower edges of the pair instability supernova (PISN) gap, the upper and lower edges of the neutron star-black hole gap, and the minimum neutron star mass. We find that although the PISN gap dominates the cosmological inference with current detectors (second generation, 2G), as shown in previous work, it is the lower mass gap that will provide the most powerful constraints in the era of Cosmic Explorer and Einstein Telescope (third generation, 3G). By using the full mass distribution, we demonstrate that degeneracies between mass evolution and cosmological evolution can be broken, unless an astrophysical conspiracy shifts all features of the full mass distribution simultaneously following the (nontrivial) Hubble diagram evolution. We find that this self-calibrating "spectral siren" method has the potential to provide precision constraints of both cosmology and the evolution of the mass distribution, with 2G achieving better than 10% precision on H(z) at z≲1 within a year and 3G reaching ≲1% at z≳2 within one month.

摘要

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验