Abstract
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.
Abstract (translated)
在天文观测数据量爆炸增长的时代，传统的光谱分析方法正面临极限挑战。为此，我们开发了深度学习框架deep-REMAP，该框架采用了一种正则化、多任务的方法来预测恒星大气参数（如有效温度$T_{\rm{eff}}$、表面重力$\log \rm{g}$和金属丰度[Fe/H]）从观测光谱中提取的信息。我们首先在PHOENIX合成光谱库上训练了一个深度卷积神经网络，然后利用迁移学习对来自MARVELS调查的小规模观测FGK矮星光谱数据进行微调。接着我们将模型应用于同一调查中的732个未表征的FGK巨星候选体。 当该框架在30颗MARVELS校准恒星上验证时，deep-REMAP能够准确地恢复有效温度（$T_{\rm{eff}}$）、表面重力（$\log \rm{g}$）和金属丰度([Fe/H])等参数，并且在$T_{\rm{eff}}$方面达到了大约75K的精度。通过结合非对称损失函数与嵌入式损失，我们的回归-分类框架具有可解释性、能够应对参数不平衡的问题，并能捕捉到非高斯不确定性。 尽管deep-REMAP最初是为MARVELS调查设计的，但该框架可以扩展至其他天文调查和合成光谱库中使用，展示了恒星表征领域的强大且自动化的方法路径。
URL
https://arxiv.org/abs/2510.09362
