On the estimation of stellar parameters with uncertainty prediction from Generative Artificial Neural Networks: application to Gaia RVS simulated spectra
On the estimation of stellar parameters with uncertainty prediction from Generative Artificial Neural Networks: application to Gaia RVS simulated spectra
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DOI：
10.1051/0004-6361/201527045
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发表时间：
2016-10-01
影响因子：
6.5
通讯作者：
Allende Prieto, C.
中科院分区：
文献类型：
--
作者：
Dafonte, C.;Fustes, D.;Allende Prieto, C.
目标。我们提出了一种创新的人工神经网络（ANN）架构，称为生成式ANN（GANN），它计算前向模型，即它学习将未知输出（在这种情况下，恒星大气参数）与给定输入（光谱）相关的函数。这样的模型可以集成在贝叶斯框架中，以估计输出的后验分布。GANN的架构遵循与普通ANN相同的方案，但输入和输出相反。我们用一组大气参数（T-eff，log g，[Fe/H]和[alpha/Fe]）训练网络，获得这些输入的恒星光谱。使用验证数据集最小化网格中的光谱和估计光谱之间的残差，以保持解决方案尽可能通用。传统的人工神经网络和GANNs的性能，估计恒星参数作为星星亮度的函数，并比较不同的银河系人口。GANN为具有丰富和中等金属含量的早期和中期光谱类型提供了显着改进的参数化。这两种算法的行为是非常相似的，我们的样本的晚型恒星，获得残差的推导[Fe/H]和[α/Fe]低于0.1德克斯的恒星与盖亚星等G（rvs）< 12，这占了数量级的400万颗恒星被观测到的盖亚卫星的径向速度摄谱仪。计算的天体物理参数的不确定性估计对于参数化本身的验证和天文学界随后的利用至关重要。GANNs不仅产生给定光谱的参数，而且还产生给定参数集的观测光谱和预测光谱之间的拟合优度。此外，他们允许我们获得完整的后验分布的天体物理参数空间，一旦噪声模型被假定。这可用于新奇检测和质量评估。
Aims. We present an innovative artificial neural network (ANN) architecture, called Generative ANN (GANN), that computes the forward model, that is it learns the function that relates the unknown outputs (stellar atmospheric parameters, in this case) to the given inputs (spectra). Such a model can be integrated in a Bayesian framework to estimate the posterior distribution of the outputs.Methods. The architecture of the GANN follows the same scheme as a normal ANN, but with the inputs and outputs inverted. We train the network with the set of atmospheric parameters (T-eff, log g, [Fe/H] and [alpha/Fe]), obtaining the stellar spectra for such inputs. The residuals between the spectra in the grid and the estimated spectra are minimized using a validation dataset to keep solutions as general as possible.Results. The performance of both conventional ANNs and GANNs to estimate the stellar parameters as a function of the star brightness is presented and compared for different Galactic populations. GANNs provide significantly improved parameterizations for early and intermediate spectral types with rich and intermediate metallicities. The behaviour of both algorithms is very similar for our sample of late-type stars, obtaining residuals in the derivation of [Fe/H] and [alpha/Fe] below 0.1 dex for stars with Gaia magnitude G(rvs) < 12, which accounts for a number in the order of four million stars to be observed by the Radial Velocity Spectrograph of the Gaia satellite.Conclusions. Uncertainty estimation of computed astrophysical parameters is crucial for the validation of the parameterization itself and for the subsequent exploitation by the astronomical community. GANNs produce not only the parameters for a given spectrum, but a goodness-of-fit between the observed spectrum and the predicted one for a given set of parameters. Moreover, they allow us to obtain the full posterior distribution over the astrophysical parameters space once a noise model is assumed. This can be used for novelty detection and quality assessment.