A study comparing the oxygen abundance of stars with planets to stars without planets has been completed. By looking at various indicators of the O abundance, it was discovered that stars with planets generally present higher oxygen abundances. These studies are part of an IAC programme to determine whether significant differences exist between the chemical compositions of stars with and without planets (Ecuvillon, Israelian et al., 2006, A&A 445, 633).
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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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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 spectra
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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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