TNFlow：用于海王星外天体表面成分的摊销后验推断
TNFlow: Amortized Posterior Inference for Trans-Neptunian Object Surface Composition
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- University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
- SETI Institute（SETI研究所）
- NASA Ames Research Center（NASA艾姆斯研究中心）
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中文总结 AI 辅助
提出TNFlow架构，基于Shkuratov模型合成光谱训练，可从TNO反射光谱推断表面成分，速度快且泛化性好，但对真实JWST光谱存在材料盲区或偏差，或源于模拟器保真度或训练集。
中文摘要 AI 辅助
我们提出了TNFlow，这是一种结合Transformer和归一化流的架构，用于从海王星外天体（TNOs）的反射光谱推断其表面成分。TNFlow基于Shkuratov辐射传输模型生成的合成光谱进行训练，以作为该模型的逆模型。TNFlow在单个CPU核心上处理一条光谱约需0.7秒，返回关于满足单纯形约束的成分和粒径的多模态后验分布。在合成光谱上，最高权重模式在测试集上与真实值的平均总变差距离为0.149，且该模型能很好地泛化到已知成分的未见过的组合。对真实JWST光谱的定性测试显示，模型对某些材料存在盲区或偏差，我们认为这可能归因于模拟器的保真度或训练集。
英文摘要
We present TNFlow, a transformer and normalizing flow architecture for inferring the surface composition of Trans-Neptunian Objects (TNOs) from their reflectance spectra. TNFlow is trained on synthetic spectra generated by the Shkuratov radiative transfer model to act as its inverse. TNFlow takes ${\sim}$0.7s to invert one spectrum on a single CPU core, returning a multimodal posterior over simplex-valid compositions and grain sizes. On synthetic spectra, the highest-weight mode achieves a mean total-variation distance of 0.149 from ground truth on the test split, and the model generalizes well to unseen combinations of known components. Qualitative tests on real JWST spectra show blindness or bias towards some materials. We suggest this could be attributed to either simulator fidelity or the training set.
