【摘要】 新一代大规模光谱巡天项目产生了近千万条低分辨率恒星光谱,基于这些光谱数据,介绍一种名为The Cannon的机器学习方法。该方法完全基于已知恒星大气参数(有效温度、表面重力加速度和金属丰度等)的光谱数据,通过数据驱动来构建特征向量,建立光谱流量特征和恒星参数的函数对应关系,进而应用到观测光谱数据中,实现对恒星光谱的大气参数求解。The Cannon的主要优势为不直接基于任何恒星物理模型,适用性更广;由于使用了全谱信息,即便对于低信噪比光谱也能得到较高可信度的参数结果,该算法在大规模恒星光谱的数据处理和参数求解方面具有明显的优势。此外,还利用The Cannon得到LAMOST光谱数据中K巨星和M巨星的恒星参数。更多还原

【Abstract】 A new generation of large sky area spectroscopic survey project has produced nearly 10 million low-resolution stellar spectra. Based on these spectroscopic data, this paper introduced a machine learning algorithm named The Cannon. The algorithm is based on the training spectra of known stellar atmospheric parameters(effective temperature, surface gravity and metalicity) to establish the relation between spectral flux characteristics and stellar parameters. Then it is applied to the observed spectral data to calculate atmospheric parameters. The main advantage of The Cannon is that it is not based on any physical model of stars strictly and has a wider applicability. Moreover, because of the use of full spectrum information, even for spectra with low signal-to-noise ratio(SNR), it can obtain more reliable parameters. This algorithm has great advantages in data processing and parameter measuring of large quantity of stellar spectroscopy. In addition, this paper presents two examples of using The Cannon to obtain stellar parameters of K and M giants using the LAMOST spectra.更多还原