基于物理约束的深度网络重建ASO-S/HXI太阳耀斑硬X射线源图像
HXI-DLA2: A Physics-Constrained Deep Learning Algorithm for the ASO-S Hard X-ray Imager
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中文总结 AI 辅助
针对ASO-S/HXI硬X射线成像的欠定反演问题，提出物理约束深度网络HXI-PINN，结合物理约束与深度学习实现更符合物理一致性的耀斑硬X射线源图像重建，框架可迁移至其他调制成像仪器。
中文摘要 AI 辅助
先进天基太阳天文台（ASO-S/Kuafu-1）上的硬X射线成像仪（HXI）从91个双栅亚准直器的调制计数中重建太阳耀斑硬X射线图像，这是一个从低维测量中恢复高维空间分布的欠定问题。传统CLEAN算法依赖点源假设，会使延展耀斑形态碎片化；现有深度学习方法学习从计数到图像的数据驱动映射，但无法保证重建结果与观测之间的物理一致性。本文从调制成像的物理结构出发，提出计数均值-形状解耦理论：单因素分析证明计数均值与总源能量正相关，归一化计数形状由源空间分布和尺度决定，二者在测量域中近似可分离（弱耦合）。基于该理论构建了物理约束反演网络HXI-PINN：将解耦后的均值和形状分别映射到网络输出的能量闭合约束和损失函数中的形状一致性约束，强制输出端的计数均值闭合以对齐观测值，同时通过形状一致性损失近似全通道正向响应。本文为欠定调制成像反演提供了结合物理约束与深度学习的反演框架，原则上可迁移至其他调制成像仪器，未来工作将扩展至全日面区域迁移和跨能段验证。
英文摘要
Solar flare hard X-ray imaging is a key diagnostic of flare energy release and electron acceleration. The Hard X-ray Imager (HXI) aboard ASO-S compresses the two-dimensional source distribution into counts measured by 91 sub-collimators, making image reconstruction an inherently underdetermined inverse problem. Conventional algorithms such as CLEAN rely on point-source priors and manual tuning, whereas recent deep-learning methods offer no guarantee that their reconstructions obey the instrument's modulation-sampling forward equation. In this work we show that the counts decompose into two nearly decoupled quantities---the counts average energy, which tracks the total source flux, and the normalized counts distribution, which encodes the source spatial structure---and we exploit this property to construct a physics-constrained network, the Hard X-ray Imager Deep Learning Algorithm 2 (HXI-DLA2). Non-negativity and exact counts-average-energy closure are enforced at the network output, while a distribution-consistency loss aligns the re-projected counts with the measurement, so that the reconstruction satisfies the forward equation by construction. Tests on simulated Gaussian sources, observed soft X-ray morphologies, and a real HXI flare event show two main improvements over existing methods: the limiting resolvable dynamic range of double sources is pushed well beyond that of conventional imaging algorithms and our previous method; and complex morphologies on which prior reconstructions degrade, such as ring-like and diffuse structures, are reliably reconstructed, with the real-event result consistent with contemporaneous SDO/AIA imaging. Embedding the instrumental forward equation as a hard constraint while learning source priors from data offers a general inversion framework for modulation imaging.
发表机构
- Yunnan Observatories, Chinese Academy of Sciences（中国科学院云南天文台）
- University of Chinese Academy of Sciences（中国科学院大学）
- Purple Mountain Observatory, Chinese Academy of Sciences（中国科学院紫金山天文台）
机构由 AI 辅助整理，请以论文原文为准。
