Deep learning application for stellar parameters determination: I-constraining the hyperparameters

Abstract

Abstract Machine learning is an efficient method for analysing and interpreting the increasing amount of astronomical data that are available. In this study, we show a pedagogical approach that should benefit anyone willing to experiment with deep learning techniques in the context of stellar parameter determination. Using the convolutional neural network architecture, we give a step-by-step overview of how to select the optimal parameters for deriving the most accurate values for the stellar parameters of stars: T eff {T}_{{\rm{eff}}} , log g \log g , [M/H], and v e sin i {v}_{e}\sin i . Synthetic spectra with random noise were used to constrain this method and to mimic the observations. We found that each stellar parameter requires a different combination of network hyperparameters and the maximum accuracy reached depends on this combination as well as the signal-to-noise ratio of the observations, and the architecture of the network. We also show that this technique can be applied to other spectral-types in different wavelength ranges after the technique has been optimized.