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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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In the 90s, the COBE satellite discovered that not all the microwave emission from our Galaxy behaved as expected. Part of this signal was later assigned to a fresh new emission component, spatially correlated with the Galactic dust emission, which showed greater importance in the microwave range of frequencies. It has been named since as “anomalous microwave emission”, or AME. The current main hypothesis to explain the AME origin is that it is emitted by small dust particles which undergo fast spinning movements. In Fernández-Torreiro et al. (2023), we study the observational properties of
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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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