Abstract We present a method for deriving the stellar fundamental parameters. It is based on a regularized sliced inverse regression (RSIR).We first tested it on noisy synthetic spectra of A, F, G, and K-type stars, and inverted simultaneously their atmospheric fundamental parameters: Teff., log g, [M/H] and v sin i. Different learning databases were calculated using a range of sampling in Teff., log g, v sin i, and [M/H]. Combined with a principal component analysis (PCA) nearest neighbors (NN) search, the size of the learning database is reduced. A Tikhonov regularization is applied, given the ill-conditioning of SIR. For all spectral types, decreasing the size of the learning database allowed us to reach internal accuracies better than PCA-based NN-search using larger learning databases. For each analyzed parameter, we have reached internal errors that are smaller than the sampling step of the parameter. We have also applied the technique to a sample of observed FGK and A stars. For a selection of well-studied stars, the inverted parameters are in agreement with the ones derived in previous studies. The RSIR inversion technique, complemented with PCA pre-processing proves to be efficient in estimating stellar parameters of A, F, G, and K-type stars.

中文翻译：

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切片逆回归：应用于基本恒星参数

摘要 我们提出了一种推导恒星基本参数的方法。它基于正则化切片逆回归 (RSIR)。我们首先在 A、F、G 和 K 型恒星的嘈杂合成光谱上对其进行了测试，并同时反演了它们的大气基本参数：Teff., log g, [M /H] 和 v sin i。使用 Teff.、log g、v sin i 和 [M/H] 中的一系列采样来计算不同的学习数据库。结合主成分分析 (PCA) 最近邻 (NN) 搜索，减少了学习数据库的大小。考虑到 SIR 的病态，应用了 Tikhonov 正则化。对于所有光谱类型，减小学习数据库的大小使我们能够比使用更大学习数据库的基于 PCA 的 NN 搜索更好地达到内部精度。对于每个分析的参数，我们已经达到了小于参数采样步长的内部误差。我们还将该技术应用于观测到的 FGK 和 A 星样本。对于一些经过充分研究的恒星，反演参数与先前研究中得出的参数一致。RSIR 反演技术与 PCA 预处理相辅相成，被证明在估计 A、F、G 和 K 型恒星的恒星参数方面是有效的。