用单根光纤数据和宽波段图像，预测银河系任意位置的光谱。
Integral Field Unit Spectroscopy with One Fiber
- 基于掩码自编码器，融合光纤位置与红移波长编码进行空间条件预测。
- 在470万张图像上训练，预测结果与真实IFU数据高度一致。
- 无需实际IFU数据即可实现类似观测效果，适合大规模星系研究。
积分场单元（IFU）光谱学可提供星系的空间分辨光谱，对理解其演化至关重要。但其高昂的观测成本限制了现有数据集规模至约10⁴个天体。本文提出一种多模态、概率性基础模型，仅需宽波段图像即可直接预测银河系任意位置的高分辨率光谱，并输出校准的不确定性。该模型基于掩码自编码器架构，注入光纤位置编码和红移感知波长编码，实现空间条件化预测。在暗能量光谱仪器（DESI）调查中470万张图像及单光纤光谱观测数据上训练，利用光纤布置的自然变异性和星系形态自相似性，实现了无需任何IFU训练数据的类IFU能力。预测的发射线通量图与阿帕奇天文台附近星系映射（MaNGA）调查的独立IFU观测高度吻合，性能接近直接在IFU数据上训练的监督基线。
原文摘要 · Abstract (English)
Integral field unit (IFU) spectroscopy provides spatially resolved spectra across galaxies, offering crucial insights into their evolution. However, its high observational cost limits current IFU datasets to $\sim 10^4$ objects. We present a multi-modal, probabilistic foundation model that predicts high-resolution spectra with calibrated uncertainties at arbitrary spatial locations within a galaxy directly from broadband images. Built on a masked autoencoder framework, our architecture injects fiber positional encodings and redshift aware wavelength encodings, enabling spatially conditioned predictions. Trained on 4.7 million images and single fiber spectroscopic observations from the Dark Energy Spectroscopic Instrument (DESI) survey, our model exploits the natural variance of fiber placements and the morphological self-similarity of galaxies to achieve IFU-like capabilities without any IFU training data. Predicted emission line flux maps match independent IFU observations from the Mapping Nearby Galaxies at APO (MaNGA) survey, with performance comparable to a supervised baseline trained directly on IFU data.
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