用物理约束神经网络从太阳观测图重建磁流体初始状态，提前预测耀斑活跃区
A Physics Informed Neural Network For Deriving MHD State Vectors From Global Active Regions Observations
- 构建基于物理约束的神经网络，结合观测与磁流体方程推导初始状态
- 发现20-30千高斯磁场与约10度带宽最符合观测，对应低阶纵模激发
- 首次实现从表面图案反推深层磁结构，为周级耀斑预警提供可能
太阳活跃区并非随机分布，而是聚集在纵向扭曲的环带状结构（'toroids')中，其信息编码于对流层底部的磁流体结构中。全局磁流体浅水对流层模型（MHD-SWT）已能定性模拟此类环带。为实现耀斑活跃区的周级早期预测，需对这些环带进行前向积分，但要求模型具备动态自洽的磁、流场及壳厚变化的初始状态向量。然而，逐日磁图仅提供环带几何形态，无法提供所需状态向量。为此，本文提出PINNBARDS——一种基于物理约束神经网络的活跃区分布模拟器，利用观测环带与MHD-SWT方程推导初始状态。基于2024年2月14日SDO/HMI逐日图，验证了神经网络收敛至物理一致且主要呈反对称性的环带结构，与观测高度吻合。尽管表面数据可确定南北环带的中心纬度与纬向宽度，但无法确定对流层底部的场强，而场强直接影响活跃区出现。通过探索大范围参数空间，发现弱场（~2 kG）时以流体主导，强场（~100 kG）则过于刚性。最佳拟合观测的结果为20-30 kG的环带场强与~10°带宽，与低阶纵向模激发一致。据我们所知，PINNBARDS是首个从表面模式重建隐藏对流层磁结构状态向量的方法，有望实现耀斑活跃区的周级预测。
原文摘要 · Abstract (English)
Solar active regions (ARs) do not appear randomly but cluster along longitudinally warped toroidal bands ('toroids') that encode information about magnetic structures in the tachocline, where global-scale organization likely originates. Global MagnetoHydroDynamic Shallow-Water Tachocline (MHD-SWT) models have shown potential to simulate such toroids, matching observations qualitatively. For week-scale early prediction of flare-producing AR emergence, forward-integration of these toroids is necessary. This requires model initialization with a dynamically self-consistent MHD state-vector that includes magnetic, flow fields, and shell-thickness variations. However, synoptic magnetograms provide only geometric shape of toroids, not the state-vector needed to initialize MHD-SWT models. To address this challenging task, we develop PINNBARDS, a novel Physics-Informed Neural Network (PINN)-Based AR Distribution Simulator, that uses observational toroids and MHD-SWT equations to derive initial state-vector. Using Feb-14-2024 SDO/HMI synoptic map, we show that PINN converges to physically consistent, predominantly antisymmetric toroids, matching observed ones. Although surface data provides north and south toroids' central latitudes, and their latitudinal widths, they cannot determine tachocline field strengths, connected to AR emergence. We explore here solutions across a broad parameter range, finding hydrodynamically-dominated structures for weak fields (~2 kG) and overly rigid behavior for strong fields (~100 kG). We obtain best agreement with observations for 20-30 kG toroidal fields, and ~10 degree bandwidth, consistent with low-order longitudinal mode excitation. To our knowledge, PINNBARDS serves as the first method for reconstructing state-vectors for hidden tachocline magnetic structures from surface patterns; potentially leading to weeks ahead prediction of flare-producing AR-emergence.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。