用深度学习自动分析恒星光谱，精准预测温度、重力和金属含量。
deep-REMAP: Probabilistic Parameterization of Stellar Spectra Using Regularized Multi-Task Learning
- 基于多任务正则化学习，将光谱转为参数预测。
- 对30颗校准星预测精度达75K，有效温度误差小。
- 适合大规模天文巡天数据自动化处理。
在海量巡天数据时代，传统光谱分析方法面临挑战。为此，我们提出 deep-REMAP，一种基于正则化多任务学习的深度学习框架，从观测光谱中预测恒星大气参数。模型在 PHOENIX 合成光谱库上训练，并通过迁移学习在 MARVELS 巡天的少量观测 FGK 主序星光谱上微调。随后应用于同巡天中732个未被表征的 FGK 巨星候选体。在30颗校准星上验证，deep-REMAP 能准确恢复有效温度（$T_{ m{eff}}$）、表面重力（$\ m{log \, g}$）和金属丰度（[Fe/H]），例如 $T_{ m{eff}}$ 精度约75 K。通过非对称损失函数与嵌入损失结合，该回归分类框架具有可解释性，对参数不平衡鲁棒，并能捕捉非高斯不确定性。尽管专为 MARVELS 设计，该框架可扩展至其他巡天与合成库，提供强大的恒星参数自动表征路径。
原文摘要 · Abstract (English)
In the era of exploding survey volumes, traditional methods of spectroscopic analysis are being pushed to their limits. In response, we develop deep-REMAP, a novel deep learning framework that utilizes a regularized, multi-task approach to predict stellar atmospheric parameters from observed spectra. We train a deep convolutional neural network on the PHOENIX synthetic spectral library and use transfer learning to fine-tune the model on a small subset of observed FGK dwarf spectra from the MARVELS survey. We then apply the model to 732 uncharacterized FGK giant candidates from the same survey. When validated on 30 MARVELS calibration stars, deep-REMAP accurately recovers the effective temperature ($T_{\rm{eff}}$), surface gravity ($\log \rm{g}$), and metallicity ([Fe/H]), achieving a precision of, for instance, approximately 75 K in $T_{\rm{eff}}$. By combining an asymmetric loss function with an embedding loss, our regression-as-classification framework is interpretable, robust to parameter imbalances, and capable of capturing non-Gaussian uncertainties. While developed for MARVELS, the deep-REMAP framework is extensible to other surveys and synthetic libraries, demonstrating a powerful and automated pathway for stellar characterization.
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