Using the Surface Brightness Fluctuation technique (SBF), IAC researchers have succeeded in detecting galaxies beyond Hubble's photometric detection limit. These new detections are fundamental for obtaining accurate information about the number of weak galaxies. This number is vital for Cosmology and for the theory of galaxy formation and evolution.
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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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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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