Our arguments deal with the early evolution of Galactic globular clusters and show why only a few of the supernovae products were retained within globular clusters and only in the most massive cases (M>106 solar masses) while less massive clusters were not contaminated at all by supernovae. Here we show that supernova blast waves evolving in a steep density gradient undergo blowout and end up discharging their energy and metals into the medium surrounding the clusters. This inhibits the dispersal and the contamination of the gas left over from a first stellar generation. Only the ejecta from well-centered supernovae, that evolves into a high-density medium available for a second stellar generation in the most massive clusters would be retained. These are likely to mix their products with the remaining gas, leading in these cases eventually to a Fe contaminated second stellar generation.
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The cosmic evolution of the barred galaxy population provides key information about the secular evolution of galaxies and the settling of rotationally dominated discs. We study the bar fraction in the SMACSJ0723.37323 (SMACS0723) cluster of galaxies at z = 0.39 using the Early Release Observations obtained with the NIRCam instrument mounted on the JWST telescope. We visually inspected all cluster member galaxies using the images from the NIRCam F200W filter. We classified the galaxies into ellipticals and discs and determine the presence of a bar. The cluster member selection was based on a
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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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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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