实现快速瞬变源的发现：用于Fink代理的千新星科学模块
Enabling the discovery of fast transients: A kilonova science module for the Fink broker
AI总结：
本文介绍Fink代理中的千新星科学模块，利用主成分特征和随机森林分类器从ZTF告警中识别快速瞬变源，并验证了30天内分类的稳健性。
AI中文摘要：
我们描述了当前在Fink代理中实现的、位于千新星（KN）科学模块核心的快速瞬变源分类算法，并报告了基于模拟星表和来自ZTF告警流的真实数据得到的分类结果。我们使用无噪声、均匀采样的模拟来构建主成分（PCs）基。来自更真实的ZTF模拟的所有光变曲线都被写为该基的线性组合。相应的系数被用作训练随机森林分类器的特征。同一方法被应用于长（>30天）和中（<30天）光变曲线。后者旨在模拟ZTF告警流中发现的数据情况。基于长光变曲线的分类实现了73.87%的精确率和82.19%的召回率。中等基线分析产生了69.30%的精确率和69.74%的召回率，从而证实了当限于30天观测时精确率结果的稳健性。在两种情况下，矮星耀发和点型Ia型超新星都是最常见的污染源。最终训练的模型被集成到Fink代理中，并一直向天文学界分发标记为KN_candidates的快速瞬变源，特别是通过GRANDMA合作组织。我们表明，专门为捕捉不同光变曲线行为而设计的特征提供了足够的信息，以区分快速（类KN）和缓慢（非类KN）演化的事件。该模块代表了多信使天文学复杂基础设施链条中的一个关键环节，Fink代理团队目前正在建立这一链条，以为Vera Rubin Observatory Legacy Survey of Space and Time的数据到来做准备。
英文摘要：
We describe the fast transient classification algorithm in the center of the kilonova (KN) science module currently implemented in the Fink broker and report classification results based on simulated catalogs and real data from the ZTF alert stream. We used noiseless, homogeneously sampled simulations to construct a basis of principal components (PCs). All light curves from a more realistic ZTF simulation were written as a linear combination of this basis. The corresponding coefficients were used as features in training a random forest classifier. The same method was applied to long (>30 days) and medium (<30 days) light curves. The latter aimed to simulate the data situation found within the ZTF alert stream. Classification based on long light curves achieved 73.87% precision and 82.19% recall. Medium baseline analysis resulted in 69.30% precision and 69.74% recall, thus confirming the robustness of precision results when limited to 30 days of observations. In both cases, dwarf flares and point Type Ia supernovae were the most frequent contaminants. The final trained model was integrated into the Fink broker and has been distributing fast transients, tagged as KN_candidates, to the astronomical community, especially through the GRANDMA collaboration. We showed that features specifically designed to grasp different light curve behaviors provide enough information to separate fast (KN-like) from slow (non-KN-like) evolving events. This module represents one crucial link in an intricate chain of infrastructure elements for multi-messenger astronomy which is currently being put in place by the Fink broker team in preparation for the arrival of data from the Vera Rubin Observatory Legacy Survey of Space and Time.
