论文标题
径向速度实验（Rave）：基于卷积神经网络的狂欢光谱的参数化
The RAdial Velocity Experiment (RAVE): Parameterisation of RAVE spectra based on convolutional neural networks
论文作者
论文摘要
在恒星的大光谱调查的背景下，数据驱动的方法是在短时间内推论数百万光谱的物理参数的关键。卷积神经网络（CNN）使我们能够将可观测值（例如光谱，恒星大小）连接到物理特性（大气参数，化学丰度或一般的标签）。我们训练了CNN，采用了恒星大气参数和Apogee DR16（分辨率r = 22500）数据作为训练集标签的化学丰度。作为输入，我们使用了中间分辨率Rave DR6光谱（R〜7500）的一部分与Apogee DR16数据以及Broad-Band all_wise和2mass光度法重叠，以及Gaia Dr2 dr2光度法和近视。我们得出了精确的大气参数TEFF，log（g）和[m/h]，以及[Fe/H]，[Alpha/M]，[mg/fe]，[Si/Fe]，[si/fe]，[al/fe]，[al/fe]和[Ni/fe]的化学丰度，420165 Rave Spectra。精度通常为TEFF的60K，log（g）中的0.06和0.02-0.04 DEX的单个化学丰度。将光度法和天文统计作为其他约束，从而从衍生标签的准确性和精度方面可改善结果。在Rave调查中，我们提供了由CNN训练的大气参数和丰度的目录，以及它们对420165星的不确定性。基于CNN的方法提供了一种结合光谱，光度法和星体数据的强大方法，而无需以恒星进化模型的形式应用任何先验。开发的程序可以将Rave Spectra的科学输出范围扩展到DR6以外的范围，并将其计划的调查（例如Gaia RVS），4个和编织。我们呼吁社区放置特定的集体重点，并为为未来的光谱调查创建公正的培训样本而努力。
In the context of large spectroscopic surveys of stars, data-driven methods are key in deducing physical parameters for millions of spectra in a short time. Convolutional neural networks (CNNs) enable us to connect observables (e.g. spectra, stellar magnitudes) to physical properties (atmospheric parameters, chemical abundances, or labels in general). We trained a CNN, adopting stellar atmospheric parameters and chemical abundances from APOGEE DR16 (resolution R=22500) data as training set labels. As input, we used parts of the intermediate-resolution RAVE DR6 spectra (R~7500) overlapping with the APOGEE DR16 data as well as broad-band ALL_WISE and 2MASS photometry, together with Gaia DR2 photometry and parallaxes. We derived precise atmospheric parameters Teff, log(g), and [M/H] along with the chemical abundances of [Fe/H], [alpha/M], [Mg/Fe], [Si/Fe], [Al/Fe], and [Ni/Fe] for 420165 RAVE spectra. The precision typically amounts to 60K in Teff, 0.06 in log(g) and 0.02-0.04 dex for individual chemical abundances. Incorporating photometry and astrometry as additional constraints substantially improves the results in terms of the accuracy and precision of the derived labels. We provide a catalogue of CNN-trained atmospheric parameters and abundances along with their uncertainties for 420165 stars in the RAVE survey. CNN-based methods provide a powerful way to combine spectroscopic, photometric, and astrometric data without the need to apply any priors in the form of stellar evolutionary models. The developed procedure can extend the scientific output of RAVE spectra beyond DR6 to ongoing and planned surveys such as Gaia RVS, 4MOST, and WEAVE. We call on the community to place a particular collective emphasis and on efforts to create unbiased training samples for such future spectroscopic surveys.
