GW170817和GW190814：最大质量上的张力
GW170817 and GW190814: tension on the maximum mass
摘要：
双星事件GW170817和GW190814的发现为中子星非旋转构型的最大质量$M_{_{\rm TOV}}$提供了宝贵的约束。然而，在GW 170817和GW 190814中测量的中子星质量的巨大差异也导致了对这种最大质量的预测之间的巨大矛盾，GW 170817表明$M_{_{\rm TOV}} \lesssim 2.3\,M_{\odot}$，而GW 190814则需要$M_{_{\rm TOV}} \gtrsim 2.5\,M_{\odot}$，如果次级是合并时的中子星（不旋转或缓慢旋转）。使用遗传算法，我们采样的多维空间的参数跨越引力波和天文观测与GW170817。与以前的估计相一致，我们发现，所有的物理量是在协议的观察，如果最大质量是在$M_{_{\rm TOV}} = 2.210^{+0.116}_{-0.123} \,M_{\odot}$范围内的$2\textrm{-}σ$的置信水平。相比之下，使用$M_{_{\rm TOV}} \gtrsim 2.5\,M_{\odot}$的最大质量，不仅需要远高于数值相对论估计的引力波发射效率，而且还导致喷射质量的显著不足。因此，可以通过假设GW 190814中的次级恒星在合并时是一个黑洞来释放张力，尽管它之前可能是一颗旋转的中子星。
英文摘要：
The detection of the binary events GW170817 and GW190814 has provided invaluable constraints on the maximum mass of nonrotating configurations of neutron stars, $M_{_{\rm TOV}}$. However, the large differences in the neutron-star masses measured in GW170817 and GW190814 has also lead to a significant tension between the predictions for such maximum masses, with GW170817 suggesting that $M_{_{\rm TOV}} \lesssim 2.3\,M_{\odot}$, and GW190814 requiring $M_{_{\rm TOV}} \gtrsim 2.5\,M_{\odot}$ if the secondary was a (non- or slowly rotating) neutron star at merger. Using a genetic algorithm, we sample the multidimensional space of parameters spanned by gravitational-wave and astronomical observations associated with GW170817. Consistent with previous estimates, we find that all of the physical quantities are in agreement with the observations if the maximum mass is in the range $M_{_{\rm TOV}} = 2.210^{+0.116}_{-0.123} \,M_{\odot}$ within a $2\textrm{-}σ$ confidence level. By contrast, maximum masses with $M_{_{\rm TOV}} \gtrsim 2.5\,M_{\odot}$, not only require efficiencies in the gravitational-wave emission that are well above the numerical-relativity estimates, but they also lead to a significant under-production of the ejected mass. Hence, the tension can be released by assuming that the secondary in GW190814 was a black hole at merger, although it could have been a rotating neutron star before.
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。
