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arXiv 2409.07539astro-ph.HE
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含污染的伽马射线暴余辉拟合策略深入研究:千新星案例分析

A detailed dive into fitting strategies for GRB afterglows with contamination: A case study with kilonovae

Wendy F. Wallace, Nikhil Sarin

AI总结:

针对伽马射线暴余辉受千新星等污染导致传统参数推断方法失效的问题,通过比较不同拟合策略,发现忽略污染会引发参数偏差,提出联合拟合余辉与千新星/超新星的贝叶斯框架提升推断可靠性。

摘要:

伽马射线暴(GRB)余辉的观测已开始频繁揭示来自千新星或超新星的污染。这种污染对从观测中推断这些现象性质的传统方法构成重大挑战。基于当前对千新星和余辉建模的知识,近红外波段和早期观测时间的观测(如预期)对观测千新星的存在及其在GRB余辉中的性质推断均提供了最大诊断能力。然而,射电和X射线的同期观测对于缩小余辉参数空间和更高效的参数估计至关重要。我们比较了不同场景下联合拟合千新星和余辉观测的不同方法。我们发现,忽略某一源的贡献(即使在该源占次要地位的场景中)可能导致参数估计存在显著偏差,但仍可能产生不引人怀疑的良好光变曲线拟合。这种偏差在对“扣除”某一源的数据进行拟合的分析中也存在。在大多数场景中,偏差小于千新星模型固有的系统不确定性,但对余辉参数而言显著,尤其是在缺乏高质量射电和X射线观测的情况下。相反,我们表明,在无法自信排除污染的任何场景中,最可靠的推断方法是联合拟合余辉和千新星/超新星,并展示了一个贝叶斯框架使这种联合分析在计算上可行。

英文摘要:

Observations of gamma-ray burst afterglows have begun to readily reveal contamination from a kilonova or a supernova. This contamination presents significant challenges towards traditional methods of inferring the properties of these phenomena from observations. Given current knowledge of kilonova and afterglow modelling, observations (as expected) with near-infrared bands and at early observing times provide the greatest diagnostic power for both observing the presence of a kilonova and inferences on its properties in gamma-ray burst afterglows. However, contemporaneous observations in radio and X-ray are critical for reducing the afterglow parameter space and for more efficient parameter estimation. We compare different methods for fitting joint kilonova and afterglow observations under different scenarios. We find that ignoring the contribution of one source (even in scenarios where the source is sub-dominant) can lead to significantly biased estimated parameters but could still produce great light curve fits that do not raise suspicion. This bias is also present for analyses that fit data where one source is "subtracted". In most scenarios, the bias is smaller than the systematic uncertainty inherent to kilonova models but significant for afterglow parameters, particularly in the absence of high-quality radio and X-ray observations. Instead, we show that the most reliable method for inference in any scenario where contamination can not be confidently dismissed is to jointly fit for both an afterglow and kilonova/supernova, and showcase a Bayesian framework to make this joint analysis computationally feasible.

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。

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