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Constraining the Hubble constant to a precision of about 1% using multi-band dark standard siren detections

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摘要 Gravitational wave signal from the inspiral of stellar-mass binary black hole can be used as standard sirens to perform cosmological inference.This inspiral covers a wide range of frequency bands,from the millihertz band to the audio-band,allowing for detections by both space-borne and ground-based gravitational wave detectors.In this work,we conduct a comprehensive study on the ability to constrain the Hubble constant using the dark standard sirens,or gravitational wave events that lack electromagnetic counterparts.To acquire the redshift information,we weight the galaxies within the localization error box with photometric information from several bands and use them as a proxy for the binary black hole redshift.We discover that Tian Qin is expected to constrain the Hubble constant to a precision of roughly 30%through detections of 10 gravitational wave events;in the most optimistic case,the Hubble constant can be constrained to a precision of<10%,assuming Tian Qin I+II.In the optimistic case,the multi-detector network of Tian Qin and LISA is capable of constraining the Hubble constant to within 5%precision.It is worth highlighting that the multi-band network of Tian Qin and Einstein Telescope is capable of constraining the Hubble constant to a precision of about 1%.We conclude that inferring the Hubble constant without bias from photo-z galaxy catalog is achievable,and we also demonstrate self-consistency using the P-P plot.On the other hand,high-quality spectroscopic redshift information is crucial for improving the estimation precision of Hubble constant.
出处 《Science China(Physics,Mechanics & Astronomy)》 SCIE EI CAS CSCD 2022年第5期114-140,共27页 中国科学:物理学、力学、天文学(英文版)
基金 supported by the Guangdong Major Project of Basic and Applied Basic Research(Grant No.2019B030302001) the National Natural Science Foundation of China(Grant Nos.12173104,11805286,and 11690022) the National Key Research and Development Program of China(Grant No.2020YFC2201400)。
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