发表机构
Harvard University; Center for Astrophysics | Harvard & Smithsonian; National Solar Observatory（哈佛大学; 哈佛-史密森天体物理中心; 国家太阳天文台）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对神经断层扫描中图像误差不能保证物理场准确的问题，提出CoroNeRF联合重建电子密度和温度场，并利用跨种子不稳定性实现无真值误差定位，在太阳日冕测试中验证了图像保真与场保真的差异及误差定位的有效性。
AI 中文摘要
用于科学断层扫描的神经场是从二维图像优化的，但实际感兴趣的量通常是潜在的二维物理场。由于前向映射是多对一的，低的二维图像误差并不一定保证正确的三维场。此外，潜在场在训练期间不直接受到监督，其误差在部署时无法与真值评估。我们开发了CoroNeRF，通过可微分的原子发射渲染器，直接从多视角、多谱线强度联合优化三维电子密度和温度场。以太阳日冕断层扫描作为受控测试平台，我们评估了物理场恢复，并测试了跨种子不稳定性是否提供了一种在推理时无需真值的局部物理场误差指标。我们强调以下两个观察结果。(i) 图像保真度并非场保真度：光谱消融实验表明，有限通道重建可以很好地拟合其可用观测，但恢复的场却显著较差，而在更丰富的公共探针上的评估则暴露了这种差异。(ii) 跨种子不稳定性在测试的匹配模型条件下对局部物理场误差进行排序，并得到稀疏化和物理信号强度对照的支持。种子偏差投影提供了补充的方向验证，但共享的前向模型不匹配仍可能导致错误的跨种子共识。这些结果表征了在受控的单场景太阳断层扫描测试平台中，联合热力学恢复以及基于种子的误差定位的有效性和局限性。
英文摘要
Neural fields for scientific tomography are optimized from 2D images, but the actual quantity of interest is often a latent 3D physical field. Because the forward map is many-to-one, low 2D image error need not certify a correct 3D field. Moreover, the latent field is not directly supervised during training, and its error cannot be evaluated against truth at deployment. We develop CoroNeRF to jointly optimize 3D electron density and temperature fields directly from multiview, multiline intensities through a differentiable atomic-emission renderer. Using solar coronal tomography as a controlled testbed, we evaluate physical-field recovery and test whether cross-seed instability provides a ground-truth-free-at-inference indicator of local physical-field error. We underscore the following two observations. (i) Image fidelity is not field fidelity: spectral ablations show that limited-channel reconstructions can fit their available observations well while recovering substantially worse fields, whereas evaluation on a common richer probe exposes the discrepancy. (ii) Cross-seed instability ranks local physical-field error across tested matched-model conditions, supported by sparsification and physical signal-strength controls. Seed-deviation projections provide complementary directional validation, but shared forward-model mismatch can still produce incorrect cross-seed consensus. These results characterize joint thermodynamic recovery and the usefulness and limits of seed-based error localization in a controlled, single-scene solar tomography testbed.
