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arXiv 2009.09534astro-ph.GA
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随机化学演化模型中的中子星合并:时间延迟分布的影响

Neutron stars mergers in a stochastic chemical evolution model: impact of time delay distributions

L. Cavallo, G. Cescutti, F. Matteucci

摘要:

利用一个考虑并合中子星作为铕污染源的随机化学演化模型,研究了星系晕中[Eu/Fe]比的演化.我们改进了我们以前的随机化学演化模型,增加了一个时间延迟分布的中子星的合并,而不是常数延迟。如果我们假设:i)延迟时间分布$\propto t^{-1.5}$,ii)每个事件的$M_{Eu}= 1.5$ × $10^{-6} M_{\odot}$,iii)中子星的祖先在$9-50M_{\odot}$范围内,iv)初始质量函数中大质量恒星的恒定分数(0.02)产生中子星合并。我们的最佳模型是通过放松点iv)并假设一个随金属丰度变化的分数而获得的。我们确认,合并的中子星和超新星作为铕生产者的混合方案可以提供一个很好的协议与数据放松的约束的分布时间延迟的合并的中子星。采用我们最好的模型,我们也重现了分散的[Eu/Fe]在一个给定的金属丰度,这取决于大质量恒星的比例,产生中子星合并。未来的高分辨率光谱调查,例如4 MOST和WEAVE,将产生必要的统计数据来最好地限制该参数。

英文摘要:

We study the evolution of the [Eu/Fe] ratio in the Galactic halo by means of a stochastic chemical evolution model considering merging neutron stars as polluters of europium. We improved our previous stochastic chemical evolution model by adding a time delay distribution for the coalescence of the neutron stars, instead of constant delays. The stochastic chemical evolution model can reproduce the trend and the observed spread in the [Eu/Fe] data with neutron star mergers as unique producers if we assume: i) a delay time distribution $\propto t^{-1.5}$, ii) a $M_{Eu}= 1.5$x$10^{-6} M_{\odot}$ per event, iii) progenitors of neutron stars in the range $9-50M_{\odot}$ and iv) a constant fraction of massive stars in the initial mass function (0.02) that produce neutron star mergers. Our best model is obtained by relaxing point iv) and assuming a fraction that varies with metallicity. We confirm that the mixed scenario with both merging neutron stars and supernovae as europium producers can provide a good agreement with the data relaxing the constraints on the distribution time delays for the coalescence of neutron stars. Adopting our best model, we also reproduce the dispersion of [Eu/Fe] at a given metallicity, which depends on the fraction of massive stars that produce neutron star mergers. Future high-resolution spectroscopic surveys, such as 4MOST and WEAVE, will produce the necessary statistics to constrain at best this parameter.

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