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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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Accretion disks around compact objects are expected to enter an unstable phase at high luminosity. One instability may occur when the radiation pressure generated by accretion modifies the disk viscosity, resulting in the cyclic depletion and refilling of the inner disk on short timescales. Such a scenario, however, has only been quantitatively verified for a single stellar-mass black hole. Although there are hints of these cycles in a few isolated cases, their apparent absence in the variable emission of most bright accreting neutron stars and black holes has been a continuing puzzle. Here
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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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