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(Seminar)太极计划数据分析系列报告之六:Inferring the population properties of binary neutron stars with gravitational-wave measurements of spin
2018-01-10     Text Size:  A

CAS Key Laboratory of Theoretical Physics

Institute of Theoretical Physics

 Chinese Academy of Sciences

Seminar

Title

题目

太极计划数据分析系列报告之六:Inferring the population properties of binary neutron stars with gravitational-wave measurements of spin

Speaker

报告人

Dr. Xingjiang Zhu

Affiliation

所在单位

School of Physics and Astronomy, Monash University ARC Centre of Excellence for Gravitational Wave Discovery (OzGrav)

Date

日期

2018-1-10 10:00

Venue

地点

ITP New Building 6420

Abstract

摘要

The recent LIGO-Virgo detection of gravitational waves from a binary neutron star merger event GW170817 and discoveries of its accompanying electromagnetic signals marked a new era for multi-messenger astronomy. In the coming years, advanced gravitational-wave detectors are likely to detect tens to hundreds of similar events. Neutron stars in binaries can possess significant spin, which is imprinted on the gravitational waveform via the effective spin parameter. We develop a fiducial model, informed by radio observations, in order to predict the effective spin of merging binaries like GW170817. We explore the astrophysical inferences made possible by gravitational-wave measurements of spin. First, using our fiducial model, we predict that around 15-40% of binary neutron stars should have non-negligible spins assuming the spin axis of the recycled neutron star aligns with total orbital angular momentum of the binary. Second, a population for which the recycled neutron star spinning at a few milliseconds periods can be distinguished from more typical systems after 10 detections. Third, with more than 50 detections, we can confidently tell whether or not the spin axis of the recycled neutron star tends to be aligned with the binary orbit. Finally, stringent constraints can be placed on neutron star magnetic field decay after 200 detections. 

Contact person

所内联系人

Zong-Kuan Guo

 

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