AI 中文总结
本研究提出概率框架\textsc{Genova}，利用双中子星并合的引力波后验样本预测千新星光谱与光变曲线，经测试可有效复现观测结果并量化不确定性。
AI 中文摘要
千新星为双中子星并合研究提供了关键的电磁观测窗口，可揭示并合系统的性质，同时探测宇宙的r过程核合成。然而迄今为止仅探测到少数千新星候选体，其中AT2017gfo是唯一与引力波事件GW170817相关联的千新星。本研究提出了\textsc{Genova}——一个直接从双中子星并合的引力波后验样本预测千新星光谱和光变曲线的概率框架。该方法采用条件归一化流，学习以源帧组分质量、潮汐形变、观测角及并合后时间为条件的静止帧光谱分布；训练过程中对 ejecta 不透明度等其他千新星模型参数进行边缘化处理，从而将这些参数的影响以预测不确定性的形式传播到预测光谱中。自洽性测试显示，该流模型复现的中值光变曲线残差通常低于~0.1星等，在~0.4至8.0天的时间范围内，预测的68%中心区间宽度之比主要保持在0.8至1.4之间。与物理特性不同的千新星模型的对比表明，该概率预测在训练所用模型之外仍能提供有效信息。本研究将\textsc{Genova}应用于GW170817/AT2017gfo，使用多波段观测数据，包括本研究新处理的来自可见光与红外天文望远镜的Y、J、K_s波段测光数据；所得预测区间大致涵盖观测结果，同时兼顾了引力波后验不确定性及边缘化千新星模型参数带来的变化。
英文摘要
Kilonovae provide a key electromagnetic window into binary neutron star mergers, revealing the properties of the merging system while probing r-process nucleosynthesis of the Universe. However, only a few kilonova candidates have been detected to date, with AT2017gfo being the only one associated with a gravitational wave event, GW170817. In this work, we present \textsc{Genova}, a probabilistic framework for predicting kilonova spectra and light curves directly from gravitational wave posterior samples of binary neutron star mergers. This method uses a conditional normalising flow to learn the distribution of rest-frame spectra conditioned on the source-frame component masses, tidal deformabilities, viewing angle and time since merger. Other kilonova model parameters, such as ejecta opacities, are marginalised over during training, so that their effects are propagated into the predicted spectra as predictive uncertainty. In the self-consistency test, the flow model reproduces the median light curves with residuals typically below $\sim 0.1$ mag, and the ratio of the predicted central 68\% interval widths remains predominantly between $0.8$ and $1.4$ over $\sim 0.4$--$8.0$ days. Comparisons with a physically distinct kilonova model show that the probabilistic prediction can remain informative beyond the model used for training. We apply \textsc{Genova} to GW170817/AT2017gfo using multi-band observations, including newly re-reduced $Y$-, $J$-, $K_s$-band photometry from the Visible and Infrared Survey Telescope for Astronomy, which we present in this work. The resulting predictive intervals broadly encompass the observations while capturing both gravitational wave posterior uncertainty and the variation induced by marginalised kilonova model parameters.
