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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 goneAdvertised on
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H II regions are ionized nebulae associated with the formation of massive stars. They exhibit a wealth of emission lines in their spectra that form the basis for estimation of chemical composition. The amount of heavy chemical elements is essential to the understanding of important phenomena such as nucleosynthesis, star formation and chemical evolution of galaxies. For over 80 years, however, a discrepancy exists of a factor of around two between heavy-element abundances (the so-called metallicity) derived from the two main kinds of emission lines that can be measured in nebular spectraAdvertised on
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Dark matter is an invisible substance that makes up more than eighty percent of the matter content of the universe. We know of its existence due to its gravitational influence, being a key ingredient to understand everything from the large-scale evolution of the universe to the formation of galaxies like the Milky Way, of which we are part of . However, very little is known about its nature, which constitutes one of the greatest unsolved problems in contemporary physics. The fuzzy dark matter model has recently been studied as a promising candidate. In this model , it is postulated that darkAdvertised on