We present time resolved low resolution spectra of BLAP-009 and BLAP-014 collected with SPRAT on the Liverpool Telescope. The spectra were median-stacked in four ranges of phase. It was intended to note the variation in the shape of the helium absorption lines as a function of phase, but they do not appear to vary with time in low resolution

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 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°