AI 中文总结
该研究结合MMS观测与全动力学3D模拟，用机器学习识别湍流中电子尺度电流片，发现其宽度的复杂分布，测试PVI作为探测器，揭示电子尺度片或有助于等离子体加热，为研究日球层湍流提供新见解。
AI 中文摘要
太阳风以湍流为特征，级联产生间歇性电流结构即电流片（CS），能有效将能量耗散到等离子体中。以往用原位航天器观测研究，但单航天器技术如增量部分方差（PVI）有局限性。结合原位观测和数值模拟可深入了解日球层湍流中间歇性结构的特性。利用磁化湍流的三维全动力学模拟，通过机器学习识别CS并发现其宽度的复杂破幂律分布。电子尺度CS占主导，宽度在2d_e附近峰值。将模拟与MMS数据比较，测试PVI作为CS探测器，虽斜交会使推断尺寸增大，但能推断CS尺度。电子尺度片的普遍存在表明它们可能总体上有助于等离子体加热。
英文摘要
The solar wind is characterized by turbulence, where a cascade produces intermittent current structures called current sheets (CS) that efficiently dissipate energy into the plasma. These have been studied with in situ spacecraft observations, but single-spacecraft techniques such as the partial variance of increments (PVI) are inherently limited since they lack spatial context. A combined analysis of in situ observations and numerical simulations can provide significant insight into the properties of intermittent structures forming in heliospheric turbulence. Understanding the size and distribution of these structures is crucial in tracing the pathways of energy dissipation and particle energization in space plasma. Using 3D fully kinetic simulations of magnetized turbulence, we identify CS via machine learning and find a complex broken-power-law distribution for the CS widths, where the power-law breaks separate ion-scale CS from electron-scale CS. Electron-scale CS dominate, with widths peaking near $2d_e$. Comparing simulations with MMS data, we test PVI as a CS detector and show it can infer CS scale, though oblique crossings inflate inferred sizes. The prevalence of electron-scale sheets suggests they may contribute to plasma heating in aggregate.
