二元透镜事件贝叶斯推断框架：完全纳入高阶效应
A Bayesian Inference Framework for Binary Lens Events Fully Incorporating Higher-Order Effects
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中文总结 AI 辅助
针对高阶效应纳入不稳定的问题，提出统一贝叶斯框架及工具gapmoe，稳定恢复物理参数，为下一代巡天大样本分析提供统计一致路径。
中文摘要 AI 辅助
高阶效应，如微透镜视差和透镜轨道运动，对于刻画微透镜行星系统的物理性质（包括其质量和距离）至关重要。在实践中，高阶效应通常仅在其信号较强时才被纳入光变曲线建模，因为在弱约束情形下纳入这些效应往往会使推断参数偏向标准银河模型所不青睐的参数空间区域。这种对物理上连续效应所采取的二元选择引入了依赖于事件的筛选函数，使星族层面的解释变得复杂，并阻碍了强、弱探测在统一框架内的联合分析。在本工作中，我们研究了与高阶效应相关的不稳定性的起源，并表明此类不稳定性可能源于光变曲线参数空间中常用的无信息先验与银河模型预测分布之间的不匹配。受此启发，我们开发了一个新的贝叶斯框架，能够以稳定且统一的方式纳入高阶效应。作为该框架的一部分，我们引入了银河先验建模引擎（gapmoe），这是一个专用工具，能够高效评估银河先验密度，并允许将其直接纳入光变曲线推断。利用模拟事件，我们证明了该框架在完全考虑微透镜视差和透镜轨道运动的情况下，能够稳健地恢复真实物理参数。该框架消除了临时模型选择的需要，并为分析下一代巡天（如罗曼太空望远镜）预期产生的大样本提供了一条可扩展且统计上一致的路径。
英文摘要
Higher-order effects, such as microlensing parallax and lens orbital motion, are essential for characterizing the physical properties of microlensing planetary systems including their masses and distances. In practice, higher-order effects are often included in light-curve modeling only when their signals are strong, because their inclusion in weakly constrained cases tends to drive the inferred parameters toward regions of parameter space that are disfavored by standard Galactic models. This binary choice over physically continuous effects introduces an event-dependent selection function that complicates population-level interpretation and prevents strong and weak detections from being combined within a single uniform framework. In this work, we investigate the origin of instabilities associated with higher-order effects, and show that such instabilities can arise from a mismatch between commonly adopted uninformative priors in the light-curve parameter space and the distributions predicted by Galactic models. Motivated by this, we develop a new Bayesian framework that enables higher-order effects to be incorporated in a stable and uniform manner. As part of this framework, we introduce Galactic Prior Modeling Engine (gapmoe), a dedicated tool that enables efficient evaluation of the Galactic prior density and allows it to be incorporated directly into the light-curve inference. Using simulated events, we demonstrate that our framework robustly recovers the true physical parameters while fully accounting for microlensing parallax and lens orbital motion. This framework eliminates the need for ad-hoc model selection and provides a scalable and statistically consistent pathway for analyzing the large samples expected from next-generation surveys such as the Roman Space Telescope.
发表机构
- Department of Earth and Space Science, Graduate School of Science, Osaka University（大阪大学理学研究科地球宇宙科学系）
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