波-望远镜技术的数据驱动不确定性量化：通用方程及其在 HelioSwarm 中的应用
Data-Driven Uncertainty Quantification of the Wave-Telescope Technique: General Equations and Application to HelioSwarm
AI总结：
本文利用贝叶斯推断扩展了 wave-telescope 技术在四至九航天器配置下的不确定性量化分析，推导出近似波矢重建误差的方程，并展示了其在九航天器配置数据中提高精度和扩展可表征波矢量级的应用。
摘要：
即将到来的 NASA 任务 HelioSwarm 将使用九个航天器，首次对跨越多个尺度的空间等离子体进行同步多点测量。利用 wave-telescope 技术，HelioSwarm 的测量将允许计算波矢-频率空间中的功率，并表征在 MHD 和离子动力学尺度下等离子体中存在的波的相关色散关系。该技术已应用于 CLUSTER 和 MMS 这两个四航天器任务，其有效性此前已在少数案例研究中得到表征。我们将此不确定性量化分析扩展到四至九个航天器的任意配置下的三维平面波。我们使用贝叶斯推断来学习方程，这些方程将近似重建波矢的误差表示为相对波矢量级、航天器配置形状和航天器数量的函数。我们展示了这些方程在从九航天器配置提取的数据中的应用，既提高了该技术的准确性，又扩展了可表征的波矢量级。
英文摘要：
The upcoming NASA mission HelioSwarm will use nine spacecraft to make the first simultaneous multi-point measurements of space plasmas spanning multiple scales. Using the wave-telescope technique, HelioSwarm's measurements will allow for both the calculation of the power in wavevector-and-frequency space and the characterization of the associated dispersion relations of waves present in the plasma at MHD and ion-kinetic scales. This technique has been applied to the four-spacecraft missions of CLUSTER and MMS and its effectiveness has previously been characterized in a handful of case studies. We expand this uncertainty quantification analysis to arbitrary configurations of four through nine spacecraft for three-dimensional plane waves. We use Bayesian inference to learn equations that approximate the error in reconstructing the wavevector as a function of relative wavevector magnitude, spacecraft configuration shape, and number of spacecraft. We demonstrate the application of these equations to data drawn from a nine-spacecraft configuration to both improve the accuracy of the technique, as well as expand the magnitudes of wavevectors that can be characterized.
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。
