AI 中文总结
该研究提出以形态学为核心的一类机器学习框架，将MMS爆发模式数据中22775个窗口压缩98.8%至270个磁层鞘电流片候选体，可高效用于磁重联候选体的预筛选。
AI 中文摘要
我们提出了一种以形态学为核心的框架，用于在磁层多尺度任务（Magnetospheric Multiscale, MMS）爆发模式数据中缩小磁重联候选体的搜索范围。研究目标是湍流磁层鞘等离子体中出现的小型、短时长、仅含电子的重联电流片。该流程分为两个阶段：第一阶段，基于最小方差分析的局部帧质量筛选器，仅保留电流片坐标定义明确的窗口；第二阶段，一类深度支持向量数据描述（Deep Support Vector Data Description）神经网络，将这些窗口与由单篇已发表参考事件通过蒙特卡洛方法生成的3000个经物理校准的合成电流片库进行评分匹配。候选体是否被纳入替代库由二阶结构函数S₂(τ)决定：仅当候选体的多尺度指纹在容差带内跟踪种子事件的指纹，同时满足少量基于形状的检查时，才会被接纳。这种以S₂(τ)为核心的构建方式，直接从明确的参考事件定义类内分布，规避了湍流磁层鞘数据中缺乏经整理的负类的问题。将该框架应用于文献中的15个磁层鞘湍流区间（共1.58小时的爆发模式覆盖数据），可将22775个滑动窗口压缩至270个候选检测结果（减少98.8%）。人工目视筛查显示，其中93个为候选重联事件，118个为类电流片结构，最终保留78%的队列用于后续研究；候选重联池远超与本文用作合理性检验的已发表重联事件目录重叠的22个检测结果。该框架旨在作为更广泛重联搜索工作流的数据缩减阶段，本文提供其初始概念验证，后续将把一类学习设计扩展至更多特征通道。
英文摘要
We present a morphology-first framework for narrowing the search for magnetic-reconnection candidates in Magnetospheric Multiscale (MMS) burst-mode data. The target is the small, short, and frequently electron-only reconnecting current sheets that occur in turbulent magnetosheath plasma. The pipeline operates in two stages. A local frame-quality gate based on minimum-variance analysis first retains only windows whose current-sheet coordinates are well defined. A one-class Deep Support Vector Data Description neural network then scores those windows against a library of 3,000 physically calibrated synthetic current sheets generated by Monte Carlo from a single published reference event. Acceptance into the surrogate library is governed by the second-order structure function $S_2(τ)$: a candidate is admitted only if its multi-scale fingerprint tracks that of the seed event inside a tolerance band, together with a small number of shape-based checks. This $S_2(τ)$-anchored construction defines the in-class distribution directly from a well-understood reference event and sidesteps the absence of a curated negative class in turbulent magnetosheath data. Applied to 15 magnetosheath turbulence intervals from the literature (1.58 h of burst-mode coverage), the framework compresses 22,775 sliding windows to 270 candidate detections (a 98.8% reduction). Manual visual screening identifies 93 of these as candidate reconnection events and a further 118 as sheet-like, retaining 78% of the queue for follow-up; the candidate-reconnection pool extends well beyond the 22 detections that overlap the published reconnection-event catalog used here as a sanity check. The framework is intended as the data-reduction stage of a broader reconnection-search workflow, offered here as an initial proof of concept before extending the one-class design to additional feature channels.
Comments28 pages, 10 figures, 2 tables. Submitted to Journal of Geophysical Research: Space Physics
