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Abstract This paper offers a comprehensive, analytical, and critically informed overview of the current state of ocean multi-use research. It delves into the origins, trajectory, and driving forces behind this emerging research field, all within the broader context of investigations addressing the management of increasingly diverse and intensifying activities at sea. The Bibliometrix R package is employed to analyze the social, geographical, and conceptual dimensions of multi-use scientific production. The results obtained are then compared to a larger corpus of publications focusing on both multiple-use Marine Protected Areas (MPAs) and Marine Spatial Planning (MSP). Finally, the paper addresses research gaps, with a particular emphasis on the transdisciplinary challenges associated with translating this new marine policy concept into practical implementation and extending its application beyond European seas. 

ABSTRACT Image simulations are essential tools for preparing and validating the analysis of current and future wide-field optical surveys. However, the galaxy models used as the basis for these simulations are typically limited to simple parametric light profiles, or use a fairly limited amount of available space-based data. In this work, we propose a methodology based on deep generative models to create complex models of galaxy morphologies that may meet the image simulation needs of upcoming surveys. We address the technical challenges associated with learning this morphology model from noisy and point spread function (PSF)-convolved images by building a hybrid Deep Learning/physical Bayesian hierarchical model for observed images, explicitly accounting for the PSF and noise properties. The generative model is further made conditional on physical galaxy parameters, to allow for sampling new light profiles from specific galaxy populations. We demonstrate our ability to train and sample from such a model on galaxy postage stamps from the HST/ACS COSMOS survey, and validate the quality of the model using a range of second- and higher order morphology statistics. Using this set of statistics, we demonstrate significantly more realistic morphologies using these deep generative models compared to conventional parametric models. To help make these generative models practical tools for the community, we introduce galsim-hub, a community-driven repository of generative models, and a framework for incorporating generative models within the galsim image simulation software. 

ABSTRACT We use observations from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) survey to explore the relationship between stellar parameters and multiplicity. We combine high-resolution repeat spectroscopy for 41 363 dwarf and subgiant stars with abundance measurements from the APOGEE pipeline and distances and stellar parameters derived using Gaia DR2 parallaxes from Sanders & Das to identify and characterize stellar multiples with periods below 30 yr, corresponding to ΔRVmax ≳ 3 km s−1, where ΔRVmax is the maximum APOGEE-detected shift in the radial velocities. Chemical composition is responsible for most of the variation in the close binary fraction in our sample, with stellar parameters like mass and age playing a secondary role. In addition to the previously identified strong anticorrelation between the close binary fraction and [Fe/H], we find that high abundances of α elements also suppress multiplicity at most values of [Fe/H] sampled by APOGEE. The anticorrelation between α abundances and multiplicity is substantially steeper than that observed for Fe, suggesting C, O, and Si in the form of dust and ices dominate the opacity of primordial protostellar discs and their propensity for fragmentation via gravitational stability. Near [Fe/H] = 0 dex, the bias-corrected close binary fraction (a < 10 au) decreases from ≈100 per cent at [α/H] = −0.2 dex to ≈15 per cent near [α/H] = 0.08 dex, with a suggestive turn-up to ≈20 per cent near [α/H] = 0.2. We conclude that the relationship between stellar multiplicity and chemical composition for sun-like dwarf stars in the field of the Milky Way is complex, and that this complexity should be accounted for in future studies of interacting binaries.