论文标题
魔术：以智能计算为指导的微透明分析
MAGIC: Microlensing Analysis Guided by Intelligent Computation
论文作者
论文摘要
由于耗时的光曲线计算和高维参数空间中的病理可能性景观，通过基于标准采样的方法对二进制微透镜曲线进行建模可能具有挑战性。在这项工作中，我们提出了魔术，这是一个机器学习框架，可有效，准确地推断出具有现实数据质量的二进制事件的微透镜参数。在魔术中，将二进制微透镜参数分为两组，并用不同的神经网络分别推断。魔术的关键特征是引入神经控制的微分方程，该方程提供了通过不规则采样和较大数据差距处理光曲线的能力。基于模拟的光曲线，我们表明魔术可以在二元质量比和分离上获得几个百分比的分数不确定性。我们还测试了一个真实的微透镜事件中的魔术。即使引入了较大的数据差距，魔术也能够定位退化解决方案。由于不规则的采样在天文学调查中很常见，因此我们的方法还对涉及时间序列的其他研究具有影响。
The modeling of binary microlensing light curves via the standard sampling-based method can be challenging, because of the time-consuming light-curve computation and the pathological likelihood landscape in the high-dimensional parameter space. In this work, we present MAGIC, which is a machine-learning framework to efficiently and accurately infer the microlensing parameters of binary events with realistic data quality. In MAGIC, binary microlensing parameters are divided into two groups and inferred separately with different neural networks. The key feature of MAGIC is the introduction of a neural controlled differential equation, which provides the capability to handle light curves with irregular sampling and large data gaps. Based on simulated light curves, we show that MAGIC can achieve fractional uncertainties of a few percent on the binary mass ratio and separation. We also test MAGIC on a real microlensing event. MAGIC is able to locate degenerate solutions even when large data gaps are introduced. As irregular samplings are common in astronomical surveys, our method also has implications for other studies that involve time series.
