标题： 使用机器学习的扩展暗天体微透镜信号 显示英文标题

标题： Microlensing signatures of extended dark objects using machine learning

摘要： 本文提出了一种基于机器学习的方法，用于检测扩展暗天体（如玻色子星、轴子微簇和子晕）的独特引力微透镜特征。我们改进了MicroLIA，这是一个针对低时间分辨率数据在微透镜调查中提出的挑战而量身定制的机器学习包。使用真实的观测时间戳，我们的模型在模拟光变曲线上进行训练，以区分点状透镜和扩展透镜的微透镜效应，以及来自其他变化亮度的天体类别。我们表明，具有相对平坦质量分布的玻色子星可以在$0.8 \lesssim r/r_E\lesssim 3$的情况下被可靠地识别。有趣的是，我们还发现，在常规观测时间分辨率下，更尖锐峰值结构（如NFW子晕）可以与点透镜明显区分开来。我们的研究结果显著提升了微透镜数据在揭示扩展暗天体神秘性质方面的潜力。代码和数据集也一并提供。 显示英文摘要

摘要： This paper presents a machine learning-based method for the detection of the unique gravitational microlensing signatures of extended dark objects, such as boson stars, axion miniclusters and subhalos. We adapt MicroLIA, a machine learning-based package tailored to handle the challenges posed by low-cadence data in microlensing surveys. Using realistic observational timestamps, our models are trained on simulated light curves to distinguish between microlensing by point-like and extended lenses, as well as from other object classes which give a variable magnitude. We show that boson stars, examples of objects with a relatively flat mass distribution, can be confidently identified for $0.8 \lesssim r/r_E\lesssim 3$. Intriguingly, we also find that more sharply peaked structures, such as NFW-subhalos, can be distinctly recognized from point-lenses under regular observation cadence. Our findings significantly advance the potential of microlensing data in uncovering the elusive nature of extended dark objects. The code and dataset used are also provided.