We construct an individual convolutional neural network architecture for each of the four stellar parameters effective temperature (Teff), surface gravity (log g), metallicity [M/H], and rotational velocity (v sin i). The networks are trained on synthetic PHOENIX-ACES spectra, showing small training and validation errors. We apply the trained

In the light of more and more new instrumentation to get a deeper insight into the universe, tons of data are collected. While traditional machine-learning methods have been used in processing stellar spectral data, such large new datasets are better handled with Deep Learning (DL) techniques. In this work, we present a Deep Convolutional Neural

We report on two state-of-the-art codes designed to characterise the spectra of late-type stars. STEPAR is a Python 3.X code that implements the EW method to calculate stellar atmospheric parameters (Teff, log(g), and [Fe/H]) of FGK stars. This code has already been extensively used to analyse high-resolution stellar spectra of thousands of Gaia

In a systematic analysis of fully convective stars observed with TESS, we detected four stars that displayed giant flares that were modulated in brightness by the stars' rapid rotation. The morphology of the modulation allowed us to directly localize these flares between 55°

Despite stellar magnetic activity being present in most cool stars, its effects on spectroscopic observations are still not well understood. Several activity indicators are routinely used to identify activity-related signals in radial velocity (RV) measurements, but not all indicators trace exactly the same effects, nor are any of them always

The first results of the application of the MANCHA code to the case of stars beyond the solar case are presented: hydrodynamical simulations of stars of spectral type K0V and M0V compared with the solar case.