用光学谱数据精准预测红外波段亮度，揭示星系多波段关联性。
The Optical and Infrared Are Connected
- 基于深度学习挖掘光学与红外的隐含关联，从SDSS光谱预测WISE红外光度。
- 预测误差χ²ₙ≈1，能准确反推AGN总光度和尘埃参数等关键属性。
- 发现现有模型存在系统偏差，适合改进星系演化与恒星形成建模研究者。
星系常被建模为具有独立光谱特征的分量组合，暗示不同波段间相关性弱，但事实并非如此。本文提出一种数据驱动模型，利用物理过程间的微弱关联，通过神经网络总结的光学SDSS光谱，精确预测红外波段的WISE光度。模型在所有WISE波段的χ²ₙ≈1，颜色预测也表现良好。可紧密约束通常需依赖红外数据的性质，如活动星系核的总光度和尘埃参数（如q_{PAH}）。我们还测试了现有光谱能量分布（SED）拟合方法对多波段关系的再现能力，发现其预测存在偏差且过于自信，可能源于模型误设；星形成率与AGN光度的关联偏差最为明显。为提升现有模型，我们识别出对预测改进起关键作用的光学谱线（如CaII、SrII、FeI、[OII]和Hα），提示星形成历史与化学富集的时序关系被当前模型错误刻画。
原文摘要 · Abstract (English)
Galaxies are often modelled as composites of separable components with distinct spectral signatures, implying that different wavelength ranges are only weakly correlated. They are not. We present a data-driven model which exploits subtle correlations between physical processes to accurately predict infrared (IR) WISE photometry from a neural summary of optical SDSS spectra. The model achieves accuracies of $χ^2_N \approx 1$ for all photometric bands in WISE, as well as good colors. We are able to tightly constrain typically IR-derived properties, e.g., the bolometric luminosities of AGN and dust parameters such as $\mathrm{q_{PAH}}$. We also test whether current SED-fitting methods reproduce such panchromatic relations, but find their predictions biased and overconfident, likely due to model misspecification, with correlated biases in star-formation rates and AGN luminosities being most evident. To help improve SED models, we determine which features of the optical spectrum are responsible for our improved predictions, and identify several lines (CaII, SrII, FeI, [OII] and H$α$), which point to the complex chronology of star formation and chemical enrichment being incorrectly modelled.
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