Can neural networks distinguish computer simulated galaxy images from observed galaxies? This is the question that has been addressed in this work. For years, reproducing the morphological diversity of galaxies has been a problem for cosmological simulations. The new generation of simulations, such as Illustris TNG, are becoming more and more realistic. But enough to fool a neural network? In this work it is shown that it does not. Using unsupervised deep generative models, it is shown that, despite the fact that realism increases greatly in the last generation of simulations and with

The solar chromosphere is the region between the relatively thin and cold photosphere (temperature of a few thousand degrees) and the hot and extended corona (temperatures above a million degrees). Although the chromosphere is much less hot than the corona, it is also significantly denser and thus much more energy is required to sustain it. Moreover, the mechanical energy necessary to heat the corona needs to traverse the chromosphere, making it a crucial interface region to understand how this energy propagates and is released in the most violent activity of the solar upper atmosphere

Modelling dust formation in single stars evolving through the carbon-star stage of the asymptotic giant branch (AGB) (i.e., the late evolutionary stages of Sun-like stars) reproduces reasonably well the mid-infrared colours and magnitudes of most of the C-rich sources in low-metallicity galaxies with known distances like the Large Magellanic Cloud (LMC). The only exception to this is a small subset of extremely red objects (EROs). An analysis of the spectral energy distributions of EROs suggests the presence of large quantities of dust, which demand gas densities in the outflow significantly