标题： ZTF源分类项目：III. 变源目录 显示英文标题

标题： The ZTF Source Classification Project: III. A Catalog of Variable Sources

摘要： 可变天体的分类为从恒星内部到星系核的各种天体物理学研究提供了深刻的见解。 兹威基暂现设施（ZTF）提供时间序列观测数据，记录了超过十亿个源的变异性。 这些数据的规模要求采用自动化方法进行全面分析。 基于先前的工作，本文报告了ZTF源分类项目（SCoPe）的结果，该项目利用人工构建的包含170,632条光变曲线的训练集，训练神经网络和XGBoost机器学习（ML）算法，以对可变ZTF源进行二元分类。 我们发现多种分类器达到了高精度和高召回率，表明它们对于77个ZTF场中的209,991,147条光变曲线预测的可靠性。 我们还确定了XGB分类中最重要特征，并比较了两种机器学习算法的性能，发现XGB分类器具有更高的精度模式。 由此产生的分类目录可供公众使用，而SCoPe开发的软件是开源且适用于未来时域巡天的。 显示英文摘要

摘要： The classification of variable objects provides insight into a wide variety of astrophysics ranging from stellar interiors to galactic nuclei. The Zwicky Transient Facility (ZTF) provides time series observations that record the variability of more than a billion sources. The scale of these data necessitates automated approaches to make a thorough analysis. Building on previous work, this paper reports the results of the ZTF Source Classification Project (SCoPe), which trains neural network and XGBoost machine learning (ML) algorithms to perform dichotomous classification of variable ZTF sources using a manually constructed training set containing 170,632 light curves. We find that several classifiers achieve high precision and recall scores, suggesting the reliability of their predictions for 209,991,147 light curves across 77 ZTF fields. We also identify the most important features for XGB classification and compare the performance of the two ML algorithms, finding a pattern of higher precision among XGB classifiers. The resulting classification catalog is available to the public, and the software developed for SCoPe is open-source and adaptable to future time-domain surveys.