ELM: AN ALGORITHM TO ESTIMATE THE ALPHA ABUNDANCE FROM LOW-RESOLUTION SPECTRA
ELM: AN ALGORITHM TO ESTIMATE THE ALPHA ABUNDANCE FROM LOW-RESOLUTION SPECTRA
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DOI：
10.3847/0004-637x/817/1/78
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发表时间：
2016-01
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影响因子：
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通讯作者：
Yude Bu;Gang Zhao;Jingchang Pan;Y. B. Kumar
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作者：
Yude Bu;Gang Zhao;Jingchang Pan;Y. B. Kumar
我们研究了一种使用极限学习机 (ELM) 算法来确定恒星 α 丰度的新颖方法。将基于ELM算法的两种方法——ELM+spectra和ELM+Lick指数——应用于ELODIE数据库中的恒星光谱，我们测量了α丰度，精度优于0.065 dex。通过将这两种方法应用于具有不同信噪比 (S/N) 和不同分辨率的光谱，我们发现 ELM+ 光谱对于分辨率下降更加稳健，而 ELM+Lick 指数对于 S/N 变化更加稳健。为了进一步验证 ELM 的性能，我们将 ELM+spectra 和 ELM+Lick 指数应用于 SDSS 谱，并以 0.10 dex 左右的精度估计 α 丰度，这与 SEGUE Stellar Parameter Pipeline 给出的结果相当。我们进一步将ELM应用于银河系球状星团（M15、M13、M71）和疏散星团（NGC 2420、M67、NGC 6791）中恒星的光谱，结果与之前的研究结果吻合良好（1σ以内）。将 ELM 与其他广泛使用的方法（包括支持向量机、高斯过程回归、人工神经网络和线性最小二乘回归）进行比较，结果表明 ELM 比其他方法更有效地利用计算资源并且更准确。
We have investigated a novel methodology using the extreme learning machine (ELM) algorithm to determine the α abundance of stars. Applying two methods based on the ELM algorithm—ELM+spectra and ELM+Lick indices—to the stellar spectra from the ELODIE database, we measured the α abundance with a precision better than 0.065 dex. By applying these two methods to the spectra with different signal-to-noise ratios (S/Ns) and different resolutions, we found that ELM+spectra is more robust against degraded resolution and ELM+Lick indices is more robust against variation in S/N. To further validate the performance of ELM, we applied ELM+spectra and ELM+Lick indices to SDSS spectra and estimated α abundances with a precision around 0.10 dex, which is comparable to the results given by the SEGUE Stellar Parameter Pipeline. We further applied ELM to the spectra of stars in Galactic globular clusters (M15, M13, M71) and open clusters (NGC 2420, M67, NGC 6791), and results show good agreement with previous studies (within 1σ). A comparison of the ELM with other widely used methods including support vector machine, Gaussian process regression, artificial neural networks, and linear least-squares regression shows that ELM is efficient with computational resources and more accurate than other methods.