LensNet：在韩国微引力透镜望远镜网络中利用循环神经网络增强实时微引力透镜事件发现能力
LensNet: Enhancing Real-time Microlensing Event Discovery with Recurrent Neural Networks in the Korea Microlensing Telescope Network
AI总结：
针对传统微引力透镜事件人工审核效率低、限制系外行星探测规模的问题，研究团队开发了适配KMTNet的多分支RNN机器学习流水线LensNet，可结合多维度上下文信息分类光变信号，分类准确率超87.5%，能实现事件早期预警以支撑后续观测。
摘要：
传统的微引力透镜事件审核方法需要训练有素的人类专家，流程复杂且耗时。这种依赖人工检查的模式往往效率低下，限制了大规模系外行星探测的能力，最终阻碍了发现速率。为解决传统微引力透镜事件审核的局限，我们开发了LensNet，这是一款专门用于区分真实微引力透镜事件与由像素拖尾、衍射尖峰等仪器伪影导致的假阳性事件的机器学习流水线。我们的系统与一个检测光通量上升趋势的初步算法协同运行，这些被标记的实例随后会被送入LensNet进行进一步分类，从而实现及时告警和后续观测。LensNet专为韩国微引力透镜望远镜网络（KMTNet）的多观测站架构定制，基于丰富的人工分类事件数据集训练，针对微引力透镜事件的早期探测与预警进行了优化，使天文学家能够及时组织后续观测。该流水线的内部模型采用多分支循环神经网络（RNN）架构，结合上下文信息评估时间序列光通量数据，这些信息包括每次观测的天空背景、目标恒星的半峰全宽、光通量误差、PSF质量标记以及大气质量。我们验证了其分类准确率超过87.5%，并预计随着训练集的扩充和算法的持续优化，性能将进一步提升。
英文摘要：
Traditional microlensing event vetting methods require highly trained human experts, and the process is both complex and time-consuming. This reliance on manual inspection often leads to inefficiencies and constrains the ability to scale for widespread exoplanet detection, ultimately hindering discovery rates. To address the limits of traditional microlensing event vetting, we have developed LensNet, a machine learning pipeline specifically designed to distinguish legitimate microlensing events from false positives caused by instrumental artifacts, such as pixel bleed trails and diffraction spikes. Our system operates in conjunction with a preliminary algorithm that detects increasing trends in flux. These flagged instances are then passed to LensNet for further classification, allowing for timely alerts and follow-up observations. Tailored for the multi-observatory setup of the Korea Microlensing Telescope Network (KMTNet) and trained on a rich dataset of manually classified events, LensNet is optimized for early detection and warning of microlensing occurrences, enabling astronomers to organize follow-up observations promptly. The internal model of the pipeline employs a multi-branch Recurrent Neural Network (RNN) architecture that evaluates time-series flux data with contextual information, including sky background, the full width at half maximum of the target star, flux errors, PSF quality flags, and air mass for each observation. We demonstrate a classification accuracy above 87.5%, and anticipate further improvements as we expand our training set and continue to refine the algorithm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
