Deep learning application for stellar parameter determination

In this third article in a series, we investigate the
need of spectra denoising for the derivation of stellar parameters.
We have used two distinct datasets for this work.
The first one contains spectra in the range of 4,450–5,400 Å
at a resolution of 42,000, and the second in the range of
8,400–8,800 Å at a resolution of 11,500. We constructed two
denoising techniques, an autoencoder, and a principal
component analysis. Using random Gaussian noise added
to synthetic spectra, we have trained a neural network to
derive the stellar parameters Teff , logg, v sini e , ξt, and [M/H]
of the denoised spectra. We find that, independently of the
denoising technique, the accuracy values of stellar parameters
do not improve once we denoise the synthetic
spectra. This is true with and without applying data augmentation
to the stellar parameters neural network