Accretion states, which are universally observed in stellar mass black holes in X-ray binaries, might be expected in active galactic nuclei (AGN). This is the case at low luminosities, when the jet-corona coupling dominates the energy output in both populations. Previous attempts to extend this framework to a wider AGN population have been

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 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

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

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