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arXiv 2101.01201astro-ph.HE
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AT2017gfo:多组元Kilonovae的贝叶斯推断和模型选择及对中子星状态方程的约束

AT2017gfo: Bayesian inference and model selection of multi-component kilonovae and constraints on the neutron star equation of state

Matteo Breschi, Albino Perego, Sebastiano Bernuzzi, Walter Del Pozzo, Vsevolod Nedora, David Radice, Diego Vescovi

摘要:

2017年8月17日观测到的二元中子星合并产生的引力波GW 170817、短$γ$射线爆发GRB 170817 A和千诺瓦AT 2017 gfo的联合探测是多信使天文学的里程碑,并提供了新的约束对中子星状态方程。我们执行贝叶斯推断和模型选择上使用半分析,多组分模型,也占非球形喷出物。观测数据有利于各向异性的几何形状,球对称的配置文件,与对数贝叶斯的因子${\sim}10^{4}$,有利于多组分模型对单组分的。最适合的模型是一个各向异性的三个组成部分的动力喷出物加上中微子和粘性风。使用从最佳拟合模型和连接喷出物属性的二进制属性的数值相对论关系推断的动力喷出物参数,我们约束的二进制质量比$q<1.54$和约化潮汐参数$120<\tildeΛ<1110$。最后,我们结合AT 2017 gfo和GW 170817的预测,将$1.4~{\rm M}_\odot$的中子星的半径限制在$12.2\pm0.5~{\rm km}$($1σ$水平)。这种预测可以进一步加强,通过改进kilonova模型的数值相关性信息。

英文摘要:

The joint detection of the gravitational wave GW170817, of the short $γ$-ray burst GRB170817A and of the kilonova AT2017gfo, generated by the the binary neutron star merger observed on August 17, 2017, is a milestone in multimessenger astronomy and provides new constraints on the neutron star equation of state. We perform Bayesian inference and model selection on AT2017gfo using semi-analytical, multi-components models that also account for non-spherical ejecta. Observational data favor anisotropic geometries to spherically symmetric profiles, with a log-Bayes' factor of ${\sim}10^{4}$, and favor multi-component models against single-component ones. The best fitting model is an anisotropic three-component composed of dynamical ejecta plus neutrino and viscous winds. Using the dynamical ejecta parameters inferred from the best-fitting model and numerical-relativity relations connecting the ejecta properties to the binary properties, we constrain the binary mass ratio to $q<1.54$ and the reduced tidal parameter to $120<\tildeΛ<1110$. Finally, we combine the predictions from AT2017gfo with those from GW170817, constraining the radius of a neutron star of $1.4~{\rm M}_\odot$ to $12.2\pm0.5~{\rm km}$ ($1σ$ level). This prediction could be further strengthened by improving kilonova models with numerical-relativity information.

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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