Galactic superwinds present a filament-like structure, with strong X ray emissions, which could not be explained until now. Using hydrodynamic calculations, it has been possible to determine that this structure is caused by the autocollimation of the material that forms the galactic superwinds. The autocollimation is caused by the interaction of material escaping from stellar superclouds, compact groups of stars revealed by the HST in which intense stellar formation has given rise to at least 100 very massive and very luminous stars. This interaction also explains the X ray emission.
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Stellar ages are key to several fields of astrophysics such as exoplanet research, galactic-archeology, and of course stellar physics. Obtaining the ages of stars is however not straightforward and requires stellar modeling. The most widely used technique only requires stellar colors or temperature and surface gravity, but the uncertainties are quite large. This technique is most efficient for stars belonging to clusters, as they were born from the same molecular cloud and share the same ages. In the last decades, based on the study of stellar acoustic waves, asteroseismology became the most
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CaII Kgrains, i.e., intermittent, short-lived (about 1 minute), periodic (2-4 minutes), pointlike chromospheric brightenings, are considered to be the manifestations of acoustic waves propagating upward from the solar surface and developing into shocks in the chromosphere. After the simulations of Carlsson and Stein, we know that hot shocked gas moving upward interacting with the downflowing chromospheric gas (falling down after having been displaced upward by a previous shock) nicely reproduces the spectral features of the CaII K profiles observed in such grains, i.e., a narrowband emission
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The amount and complexity of data delivered by modern galaxy surveys has been steadily increasing over the past years. New facilities will soon provide imaging and spectra of hundreds of millions of galaxies. Extracting coherent scientific information from these large and multi-modal data sets remains an open issue for the community and data-driven approaches such as deep learning have rapidly emerged as a potentially powerful solution to some long lasting challenges. This enthusiasm is reflected in an unprecedented exponential growth of publications using neural networks, which have gone
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