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首页> 外文期刊>Astronomy and astrophysics >On the estimation of stellar parameters with uncertainty prediction from Generative Artificial Neural Networks: application to Gaia RVS simulated spectra
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On the estimation of stellar parameters with uncertainty prediction from Generative Artificial Neural Networks: application to Gaia RVS simulated spectra

机译：基于生成人工神经网络的不确定性预测恒星参数估计：在Gaia RVS模拟光谱中的应用

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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 (Teff, log?g, [Fe/H] and [α/ 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 [α/ Fe] below 0.1 dex for stars with Gaia magnitude Grvs < 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.
机译：目的我们提出了一种创新的人工神经网络（ANN）架构，称为Generative ANN（GANN），该架构可计算正向模型，即它学习将未知输出（在这种情况下为恒星大气参数）与给定输入相关联的函数（光谱）。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。方法。 GANN的架构遵循与普通ANN相同的方案，但输入和输出却被颠倒了。我们使用一组大气参数（Teff，log？g，[Fe / H]和[α/ Fe]）训练网络，以获得此类输入的恒星光谱。使用验证数据集将网格中的光谱与估计的光谱之间的残差最小化，以保持溶液尽可能通用。结果。介绍了常规ANN和GANN估算恒星参数随恒星亮度变化的性能，并针对不同的银河种群进行了比较。 GANN为具有丰富和中间金属性的早期和中间光谱类型提供了显着改善的参数设置。两种算法的行为对于我们的晚型恒星样本非常相似，对于Gaia量级Grvs <12的恒星，在[Fe / H]和[α/ Fe]的推导中获得的残差低于0.1 dex。盖亚卫星的径向速度光谱仪将观测到四百万颗星的数量。结论。计算天文参数的不确定性估计对于参数化本身的验证以及随后的天文学界的利用至关重要。 GANN不仅会生成给定频谱的参数，而且还会生成给定参数集的观测频谱与预测频谱之间的拟合优度。而且，一旦假设了噪声模型，它们使我们能够获得天文学参数空间上的全部后验分布。这可以用于新颖性检测和质量评估。

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