Interpolating detailed simulations of kilonovae: Adaptive learning and parameter inference applications
Interpolating detailed simulations of kilonovae: Adaptive learning and parameter inference applications
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DOI：
10.1103/physrevresearch.4.013046
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发表时间：
2021-05
影响因子：
4.2
通讯作者：
M. Ristić;E. Champion;R. O’Shaughnessy;R. Wollaeger;O. Korobkin;E. Chase;Chris L. Fryer;A. Hungerford
中科院分区：
文献类型：
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作者：
M. Ristić;E. Champion;R. O’Shaughnessy;R. Wollaeger;O. Korobkin;E. Chase;Chris L. Fryer;A. Hungerford
千新星的详细辐射传输模拟很难直接应用于观测;它们只粗略地涵盖了模拟参数，如喷出物的质量、速度、形态和成分。另一方面，半解析模型kilonovae可以连续评估模型参数，但忽略了重要的物理细节，没有纳入模拟，从而引入系统偏差。从覆盖广泛的喷出物属性的kilonova光变曲线的二维各向异性模拟网格开始，我们应用自适应学习技术来迭代地选择新的模拟，并为这些模拟生成高保真度的代理模型。这些替代模型允许跨模型参数进行连续评估，同时保留有关喷出物的微物理细节。使用一个新的代码多信使推断，我们演示了如何使用我们的插值模型来推断kilonova参数。与使用简化解析模型的推论相比，我们恢复了不同的喷出物属性。我们讨论了这种分析的影响，这是定性一致的类似以前的工作，使用详细的喷出物不透明度计算，并说明了系统的挑战kilonova建模。相关的数据和代码版本提供了我们的插值光曲线模型，插值实现，可用于重现我们的工作或扩展到新的模型，以及我们的多信使参数推理引擎。
Detailed radiative transfer simulations of kilonovae are difficult to apply directly to observations; they only sparsely cover simulation parameters, such as the mass, velocity, morphology, and composition of the ejecta. On the other hand, semianalytic models for kilonovae can be evaluated continuously over model parameters, but neglect important physical details which are not incorporated in the simulations, thus introducing systematic bias. Starting with a grid of 2D anisotropic simulations of kilonova light curves covering a wide range of ejecta properties, we apply adaptive-learning techniques to iteratively choose new simulations and produce high-fidelity surrogate models for those simulations. These surrogate models allow for continuous evaluation across model parameters while retaining the microphysical details about the ejecta. Using a new code for multimessenger inference, we demonstrate how to use our interpolated models to infer kilonova parameters. Comparing to inferences using simplified analytic models, we recover different ejecta properties. We discuss the implications of this analysis which is qualitatively consistent with similar previous work using detailed ejecta opacity calculations and which illustrates systematic challenges for kilonova modeling. An associated data and code release provides our interpolated light-curve models, interpolation implementation which can be applied to reproduce our work or extend to new models, and our multimessenger parameter inference engine.