Title: Gaia GraL: Gaia gravitational lens systems IX. Using XGBoost to explore the Gaia Focused Product Release GravLens catalogue Show Chinese title

Title: Gaia GraL：第九部分 Gaia 引力透镜系统。 使用 XGBoost 探索 Gaia 专注产品发布引力透镜目录

Abstract: Aims. Quasar strong gravitational lenses are important tools for putting constraints on the dark matter distribution, dark energy contribution, and the Hubble-Lemaitre parameter. We aim to present a new supervised machine learning-based method to identify these lenses in large astrometric surveys. The Gaia Focused Product Release (FPR) GravLens catalogue is designed for the identification of multiply imaged quasars, as it provides astrometry and photometry of all sources in the field of 4.7 million quasars. Methods. Our new approach for automatically identifying four-image lens configurations in large catalogues is based on the eXtreme Gradient Boosting classification algorithm. To train this supervised algorithm, we performed realistic simulations of lenses with four images that account for the statistical distribution of the morphology of the deflecting halos as measured in the EAGLE simulation. We identified the parameters discriminant for the classification and performed two different trainings, namely, with and without distance information. Results. The performances of this method on the simulated data are quite good, with a true positive rate and a true negative rate of about 99.99% and 99.84%, respectively. Our validation of the method on a small set of known quasar lenses demonstrates its efficiency, with 75% of known lenses being correctly identified. We applied our algorithm (both trainings) to more than 0.9 million quadruplets selected from the Gaia FPR GravLens catalogue. We derived a list of 1127 candidates with at least one score larger than 0.75, where each candidate has two scores -- one from the model trained with distance information and one from the model trained without distance information -- and including 201 very good candidates with both high scores. Show Chinese abstract

Abstract: 目标。 类星体强引力透镜是用于限制暗物质分布、暗能量贡献和哈勃-勒梅特定律参数的重要工具。 我们的目标是提出一种基于监督机器学习的新方法，以在大型天体测量调查中识别这些透镜。 Gaia聚焦产品发布（FPR）引力透镜目录旨在识别多重成像的类星体，因为它提供了470万个类星体领域中所有源的天体测量和测光数据。 方法。 我们新提出的在大型目录中自动识别四像透镜配置的方法基于极端梯度提升分类算法。 为了训练这个监督算法，我们进行了考虑EAGLE模拟中测量的偏折晕的形态统计分布的真实透镜模拟。 我们确定了分类的判别参数，并进行了两种不同的训练，即使用和不使用距离信息。 结果。 该方法在模拟数据上的表现非常好，真正例率和真负例率分别约为99.99%和99.84%。 我们在一个小的已知类星体透镜集上验证了该方法，证明了其效率，其中75%的已知透镜被正确识别。 我们将我们的算法（两种训练）应用于从Gaia FPR GravLens目录中选择的超过90万个四重态。 我们得出了一个包含1127个候选者的列表，每个候选者至少有一个分数大于0.75，每个候选者有两个分数——一个来自使用距离信息训练的模型，另一个来自不使用距离信息训练的模型——其中包括201个两个分数都很高的非常优秀的候选者。