arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2004.07244astro-ph.HE
收藏

核塌缩超新星的$γ$射线沉积历史

The $γ$-ray deposition histories of core-collapse supernovae

Amir Sharon, Doron Kushnir

摘要:

膨胀超新星(SN)喷出物中的$γ$射线沉积历史主要用于约束Ia型SN模型。在这里,我们将这种方法扩展到核心塌陷SNe,包括剥离的信封(SE;类型Ib/Ic/IIb)和类型IIP SNe。我们从文献中使用光度法构建热光变曲线,并使用Katz积分来提取$γ$射线沉积历史。我们恢复了Ia型SNe的$γ$射线逃逸时间$t_0\approx30-45\,\textrm{d}$的紧范围,并发现了SE型SNe的新的紧范围$t_0\approx80-140\,\textrm{d}$。用$t_0\gtrsim400\,\textrm{d}$可将IIP型SNe与其它SNe类型明显区分开,并且$t_0$与合成的$^{56}$ Ni质量之间可能存在负相关。我们发现合成的$^{56}$ Ni在SE SNe中的典型质量大于在IIP SNe中的典型质量,与Kushnir的结果一致。这不利于具有相同初始质量范围的这些爆炸的祖先。我们恢复观测值的$ET$,时间加权积分亮度从冷却排放,IIP型,我们发现的暗示非零$ET$值在一些SE SNe。我们应用一个简单的$ γ$ -射线辐射传输代码计算的$γ$ -射线沉积历史的模型从文献中,我们表明,所观察到的历史是一个强大的工具,约束模型。

英文摘要:

The $γ$-ray deposition history in an expanding supernova (SN) ejecta has been mostly used to constrain models for Type Ia SN. Here we expand this methodology to core-collapse SNe, including stripped envelope (SE; Type Ib/Ic/IIb) and Type IIP SNe. We construct bolometric light curves using photometry from the literature and we use the Katz integral to extract the $γ$-ray deposition history. We recover the tight range of $γ$-ray escape times, $t_0\approx30-45\,\textrm{d}$, for Type Ia SNe, and we find a new tight range $t_0\approx80-140\,\textrm{d}$, for SE SNe. Type IIP SNe are clearly separated from other SNe types with $t_0\gtrsim400\,\textrm{d}$, and there is a possible negative correlation between $t_0$ and the synthesized $^{56}$Ni mass. We find that the typical masses of the synthesized $^{56}$Ni in SE SNe are larger than those in Type IIP SNe, in agreement with the results of Kushnir. This disfavours progenitors with the same initial mass range for these explosions. We recover the observed values of $ET$, the time-weighted integrated luminosity from cooling emission, for Type IIP, and we find hints of non-zero $ET$ values in some SE SNe. We apply a simple $ γ$-ray radiation transfer code to calculate the $γ$-ray deposition histories of models from the literature, and we show that the observed histories are a powerful tool for constraining models.

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。

↑