While microlensing is very rare, occurring on average once per million stars observed, current and near-future surveys are coming online with the capability of providing photometry of almost the entire visible sky to depths up to R $\sim$22 mag or fainter every few days, which will contribute to the detection of black holes and exoplanets through follow-up observations of microlensing events. Based on galactic models, we can expect microlensing events across a vastly wider region of the galaxy, although the cadence of these surveys (2-3 $d^{-1}$) is lower than traditional microlensing surveys, making efficient detection a challenge. Rapid advances are being made in the utility of time-series data to detect and classify transient events in real-time using very high data-rate surveys, but limited work has been published regarding the detection of microlensing events, particularly for when the data streams are of relatively low-cadence. In this research, we explore the utility of a Random Forest algorithm for identifying microlensing signals using time-series data, with the goal of creating an efficient machine learning classifier that can be applied to search for microlensing in wide-field surveys even with low-cadence data. We have applied and optimized our classifier using the OGLE-II microlensing dataset, in addition to testing with PTF/iPTF survey data and the currently operating ZTF, which applies the same data handling infrastructure that is envisioned for the upcoming LSST.

尽管微透镜很少见，平均每百万颗恒星发生一次，但当前和不久的将来的测量都可以在线进行，它能够对几乎整个可见天空进行深度达R的测光。 $〜$每隔22 mag或微弱，这将有助于通过对微透镜事件的后续观察来探测黑洞和系外行星。基于银河模型，尽管这些调查的节奏（2-3），我们可以预期银河系广阔范围内的微透镜事件$d^{-\mathrm{1个}}$）低于传统的微透镜调查，因此很难进行有效的检测。时间序列数据的实用性正在快速发展，以使用非常高的数据速率调查实时检测和分类瞬态事件，但是关于微透镜事件的检测（尤其是在数据流时）的工作已发表得很少。节奏相对较低。在这项研究中，我们探索了一种随机森林算法来使用时间序列数据识别微透镜信号的实用性，目的是创建一个有效的机器学习分类器，该分类器甚至可以在低视场条件下用于宽视场调查中的微透镜搜索。节奏数据。除了使用PTF / iPTF调查数据和当前运行的ZTF进行测试外，我们还使用OGLE-II微透镜数据集应用和优化了分类器，