论文标题
用于光谱信息开发的模型RRNET和杆中等分辨率频谱参数估计
A Model RRNet for Spectral Information Exploitation and LAMOST Medium-resolution Spectrum Parameter Estimation
论文作者
论文摘要
这项工作提出了一个残留的复发性神经网络（RRNET），用于合成提取光谱信息，并估算出恒星大气参数以及15个来自大型天空区域多目标光纤光谱望远镜（Lamost）的中分辨率光谱的化学元素丰度。 RRNET由两个基本模块组成：一个残差模块和一个复发模块。残留模块根据参数的纵向驱动力提取光谱特征，而复发模块恢复了光谱信息并限制了基于跨波段信念增强的噪声的负面影响。 RRNET受Lamost DR7和Apogee-Payne目录之间的普通恒星的光谱训练。预计将有17个恒星参数及其不确定性的237万个中分辨率DR7的中分辨率光谱。对于S/N> = 10的光谱，TEFF和LOG G的估计精度分别为88 K和0.13 DEX，元素C，MG，Al，Si，Ca，Ca，Fe，Ni为0.05 DEX至0.08 DEX，以及N，O，S，S，K，K，Ti，Ti，Ti，Cr，Mn为0.09 Dex至0.14 Dex，dex dex dex dex dex dex。与Starnet和Spcanet相比，RRNet显示出更高的精度和鲁棒性。与Hermes调查的Apache Point观测值分子演化实验和银河考古学相比，RRNET在合理的偏见范围内表现出良好的一致性。最后，这项工作从Lamost DR7，源代码，经过训练的模型以及天文学科学探索和数据处理算法研究参考的237万个中分辨率的光谱释放了目录。
This work proposes a Residual Recurrent Neural Network (RRNet) for synthetically extracting spectral information, and estimating stellar atmospheric parameters together with 15 chemical element abundances for medium-resolution spectra from Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). The RRNet consists of two fundamental modules: a residual module and a recurrent module. The residual module extracts spectral features based on the longitudinally driving power from parameters, while the recurrent module recovers spectral information and restrains the negative influences from noises based on Cross-band Belief Enhancement. RRNet is trained by the spectra from common stars between LAMOST DR7 and APOGEE-Payne catalog. The 17 stellar parameters and their uncertainties for 2.37 million medium-resolution spectra from LAMOST DR7 are predicted. For spectra with S/N >= 10, the precision of estimations Teff and log g are 88 K and 0.13 dex respectively, elements C, Mg, Al, Si, Ca, Fe, Ni are 0.05 dex to 0.08 dex, and N, O, S, K, Ti, Cr, Mn are 0.09 dex to 0.14 dex, while that of Cu is 0.19 dex. Compared with StarNet and SPCANet, RRNet shows higher accuracy and robustness. In comparison to Apache Point Observatory Galactic Evolution Experiment and Galactic Archaeology with HERMES surveys, RRNet manifests good consistency within a reasonable range of bias. Finally, this work releases a catalog for 2.37 million medium-resolution spectra from the LAMOST DR7, the source code, the trained model and the experimental data respectively for astronomical science exploration and data processing algorithm research reference.
