Ia型超新星SN 2002 bo的重建
Probabilistic Reconstruction of Type Ia Supernova SN 2002bo
摘要：
手动拟合Ia型超新星的光谱时间序列提供了一种从参数模型重建爆炸的方法，但由于缺乏关于模型不确定性或参数简并的信息，理论和观测之间的直接比较是困难的。为了解决这个重要问题，我们提出了一种新的方法来概率重建正常Ia型超新星SN 2002 bo的外部喷出物。一个单一的时代的光谱，采取10天前最大的光，适合一个13参数模型描述的元素组成的喷出物和爆炸物理（密度，温度，速度和爆炸时期）。模型评估是通过应用一种新的快速光谱合成技术，其中的辐射转移代码，TARDIS，是由机器学习框架加速。后验分布的分析揭示了一个复杂的和退化的参数空间，并允许直接比较各种流体动力学模型。我们的分析有利于爆燃的情况下，我们发现，我们的技术提供了一种新的方式来比较模拟观察。
英文摘要：
Manual fits to spectral times series of Type Ia supernovae have provided a method of reconstructing the explosion from a parametric model but due to lack of information about model uncertainties or parameter degeneracies direct comparison between theory and observation is difficult. In order to mitigate this important problem we present a new way to probabilistically reconstruct the outer ejecta of the normal Type Ia supernova SN 2002bo. A single epoch spectrum, taken 10 days before maximum light, is fit by a 13-parameter model describing the elemental composition of the ejecta and the explosion physics (density, temperature, velocity, and explosion epoch). Model evaluation is performed through the application of a novel rapid spectral synthesis technique in which the radiative transfer code, TARDIS, is accelerated by a machine-learning framework. Analysis of the posterior distribution reveals a complex and degenerate parameter space and allows direct comparison to various hydrodynamic models. Our analysis favors detonation over deflagration scenarios and we find that our technique offers a novel way to compare simulation to observation.
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。
