AI 中文总结
本研究推出NMMA-Astro-COLIBRI自动分类服务，通过贝叶斯方法结合NMMA与Astro-COLIBRI平台，以超新星模板库实现暂现源分类，在SN 2021ugl案例中验证了其对千新星的区分能力。
AI 中文摘要
来自大视场巡天的公开测光警报数量激增，亟需用于实时暂现源分类的自动化工具。我们推出NMMA-Astro-COLIBRI，这是一种按需贝叶斯分类服务，将核物理与多信使天体物理学（NMMA）推理框架与Astro-COLIBRI实时多信使平台相结合。在光学暂现源被探测到后，若有测光数据，会对其进行质量筛选；筛选后的测光数据通过嵌套采样，与11个超新星模板库中用户选定的模型进行拟合，结果会在数分钟内交付给所有用户。对同一光学暂现源应用两种或更多模型时，该服务会报告对应的对数贝叶斯因子，作为竞争亚型的定量排序。我们以SN 2021ugl（ZTF21abotose）为例展示该工作流程，这是一颗IIb型超新星，最初被自动化实时管道误判为千新星候选体，我们对比了竞争的超新星和千新星模型。在仅使用两个波段（ZTF g和r）最初约6天测光数据的早期配置中，即光谱确认前10天，经验IIb型模板恢复了正确分类，优于千新星模板和模仿千新星的激波冷却模型；在完整的47天三波段基线配置中，它再次在所有竞争的超新星和千新星模板中获得最高证据。这些结果凸显了多巡天时代全面的超新星模板库对千新星区分的重要性。
英文摘要
The surge in publicly available photometric alerts from wide-field surveys requires automated tools for real-time transient classification. We present NMMA--Astro-COLIBRI, an on-demand Bayesian classification service that couples the Nuclear-physics and Multi-Messenger Astrophysics (NMMA) inference framework to the Astro-COLIBRI real-time multi-messenger platform. After the detection of an optical transient, if photometry is available, it is quality-filtered. The filtered photometry is fitted by nested sampling against a user-selected model from a library of eleven supernova templates; results are delivered to every user within minutes. Applying two or more models on the same optical transient, the service reports the corresponding log Bayes factors as a quantitative ranking of competing subtypes. We demonstrate the workflow on SN 2021ugl (ZTF21abotose), a Type IIb supernova initially mistaken for a kilonova candidate by automated real-time pipelines, comparing competing supernova and kilonova models. In an early-time configuration using only the first ~ 6 days of photometry in two bands (ZTF g and r), so ten days before spectroscopic confirmation, the empirical Type IIb template recovers the correct classification, favored over both the kilonova template and the kilonova-mimicking shock-cooling model. In the full 47-day, three-band baseline, it again achieves the highest evidence over every competing supernova and kilonova template. These results highlight the importance of a comprehensive supernova template library for kilonova discrimination in the multi-survey era.
