Generating stellar spectra using Neural Networks

A new generative technique is presented in this paper that uses Deep Learning to reconstruct stellar spectra based on a set of stellar parameters. Two different Neural Networks were trained allowing the generation of new spectra. First, an autoencoder is trained on a set of BAFGK synthetic data calculated using ATLAS9 model atmospheres and SYNSPEC radiative transfer code. These spectra are calculated in the wavelength range of Gaia RVS between 8 400 and 8 800 {\AA}. Second, we trained a Fully Dense Neural Network to relate the stellar parameters to the Latent Space of the autoencoder. Finally, we linked the Fully Dense Neural Network to the decoder part of the autoencoder and we built a model that uses as input any combination of $T_{eff}$, $\log g$, $v_e \sin i$, [M/H], and $\xi_t$ and output a normalized spectrum. The generated spectra are shown to represent all the line profiles and flux values as the ones calculated using the classical radiative transfer code. The accuracy of our technique is tested using a stellar parameter determination procedure and the results show that the generated spectra have the same characteristics as the synthetic ones.