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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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It is well known that fullerenes – big, complex, and highly resistant carbon molecules with potential applications in nanotechnology – are mostly seen in planetary nebulae (PNe); old dying stars with progenitor masses similar to our Sun. Fullerenes, like C60 and C70, have been detected in PNe whose infrared (IR) spectra are dominated by broad unidentified IR (UIR) plateau emissions. The identification of the chemical species (structure and composition) responsible for such UIR emission widely present in the Universe is a mystery in astrochemistry; although they are believed to be carbon-rich
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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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