At the heart of the computation of model atmospheres there is the so-called Stellar Atmosphere Problem, which consists of the self-consistent solution of the radiative transfer equations under specific constraints. The amazing progresses achieved in the field since the 1970s are due to both the dramatic increase of the computational facilities and

Conventionally, galaxy morphological classifications are defined by visual assessment. However, visual classification systems such as Hubble types can be intrinsically biased due to the subjective judgement of human classifiers. Additionally, since morphological "classifications" into types is an important and complementary process, it is not clear

As available data sets grow in size and complexity, advanced visualization tools enabling their exploration and analysis become more important. In modern astronomy, integral field spectroscopic galaxy surveys are a clear example of increasing dimensionality and complexity of datasets, which challenge the traditional methods used to extract the

Galaxies exhibit a wide variety of morphologies which contain valuable information about their star formation histories. Having large samples of morphologically classified galaxies is fundamental to understand their formation and evolution. Deep learning algorithms have proven to be extremely successful for morphological classification of galaxies

Bill Gray of Project Pluto brought to our attention an error of 0.03° in the listed latitude of our Kitt Peak telescope. While correcting the table where this occurred, we also take the opportunity to update the instrument properties and weather statistics of our remote telescopes.

High-resolution observations of the Sun reveal the presence of Magnetic Bright Points (MBPs), which are small-scale features associated with strong magnetic field regions, that are found all over the solar photosphere. In this work, we characterize some physical properties and dynamics of MBPs in a quiet Sun region by using time series of images