利用Redback探索结合建模策略研究带相对论喷流的超新星的能力
Exploring the capabilities of combined modelling strategies for supernova with relativistic jets using Redback
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中文总结 AI 辅助
该研究对比三种单独建模策略与联合建模策略在GRB-SN光变曲线参数估计中的表现，发现联合建模更可靠，尤其在多波段X射线和射电观测可用时。
中文摘要 AI 辅助
与相对论喷流关联的超新星（SN）事件呈现多成分光变曲线。过去由于建模和推断的局限性，这类复杂光变曲线的分析大多通过单独研究每个成分来进行。这种解耦分析易因相对论喷流与超新星辐射的交叉污染导致参数估计出现偏差。此外，所采用方法的差异使得不同天体种群间的物理参数难以比较。本研究针对伽马射线暴（GRB）与超新星关联事件，调查三种常用的建模策略，并将其与联合建模和推断方法进行对比。我们利用Redback评估每种方法拟合和复现1000个模拟GRB-SN数据集真实注入参数的能力。我们将这些数据集分为三种常见的GRB-SN光变曲线形态：超新星占主导、伽马射线暴占主导，以及超新星与伽马射线暴对光变曲线贡献相当的情况。我们还研究了每种建模技术在伽马射线暴余辉喷流断裂发生在早期和晚期时的表现。研究发现，即使单独模型对数据的最佳拟合结果看似可接受，将各成分独立处理也会导致估计参数出现显著偏差。我们进一步证明，余辉扣除对于GRB-SN建模并不可靠，因为余辉拟合的不确定性未完全传递到扣除余辉后的光变曲线中。相反，我们的结果表明，联合建模相对论喷流和超新星成分是最可靠的建模方法，尤其在有多波段X射线和射电观测可用时。
英文摘要
Events in which a supernova (SN) is associated with a relativistic jet present multi-component light curves. In the past, the analysis of such complex light curves was carried out largely by investigating each component individually due to limitations in modelling and inference. Such decoupled analyses are susceptible to biased parameter estimation due to cross-contamination between the emission of the relativistic jet and the SN. Additionally, differences in adopted methodologies make it difficult to compare physical parameters across populations. In this work, we investigate three common modelling strategies adopted for events where a Gamma-ray Burst (GRB) is associated with an SN and compare them with a joint model and inference approach. We assess the ability of each approach to fit and reproduce the true injection parameters of 1000 simulated GRB-SN datasets using Redback. We split these datasets into three common GRB-SN light curve morphologies: one where the SN is dominant, one where the GRB is dominant, and one where the SN and GRB have relatively equal contributions to the light curve. We also investigate the behaviour of each modelling technique when the jet break of the GRB afterglow occurs at early times and at late times. We find that treating each component independently with an individual model leads to significant bias in the estimated parameters even though the resulting best fit to the data may appear acceptable. We further demonstrate that afterglow subtraction is unreliable for GRB-SN modelling as uncertainties in the afterglow fit are not fully propagated into the resulting afterglow-subtracted light curve. Instead, our results show that jointly modelling the relativistic jet and SN components provides the most reliable modelling approach, particularly when multi-band X-ray and radio observations are available.
