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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 itlearns the function that relates the unknown outputs (stellar atmosphericparameters, in this case) to the given inputs (spectra). Such a model can beintegrated in a Bayesian framework to estimate the posterior distribution ofthe outputs. Methods. The architecture of the GANN follows the same scheme as anormal ANN, but with the inputs and outputs inverted. We train the network withthe set of atmospheric parameters (Teff, logg, [Fe/H] and [alpha/Fe]),obtaining the stellar spectra for such inputs. The residuals between thespectra in the grid and the estimated spectra are minimized using a validationdataset to keep solutions as general as possible. Results. The performance ofboth conventional ANNs and GANNs to estimate the stellar parameters as afunction of the star brightness is presented and compared for differentGalactic populations. GANNs provide significantly improved parameterizationsfor early and intermediate spectral types with rich and intermediatemetallicities. The behaviour of both algorithms is very similar for our sampleof late-type stars, obtaining residuals in the derivation of [Fe/H] and[alpha/Fe] below 0.1dex for stars with Gaia magnitude Grvs<12, which accountsfor a number in the order of four million stars to be observed by the RadialVelocity Spectrograph of the Gaia satellite. Conclusions. Uncertaintyestimation of computed astrophysical parameters is crucial for the validationof the parameterization itself and for the subsequent exploitation by theastronomical community. GANNs produce not only the parameters for a givenspectrum, but a goodness-of-fit between the observed spectrum and the predictedone for a given set of parameters. Moreover, they allow us to obtain the fullposterior distribution...

astronomy and astrophysics

On the estimation of stellar parameters with uncertainty prediction from  Generative Artificial Neural Networks: application to Gaia RVS simulated  spectra