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Abstract:

Planetesimals inevitably bear the signatures of their natal environment, preserving in their composition a record of the metallicity of their system's original gas and dust, albeit one altered by the formation processes. When planetesimals are dispersed from their system of origin, this record is carried with them. As each star is likely to contribute at least 10¹² interstellar objects (ISOs), the Galaxy's drifting population of ISOs provides an overview of the properties of its stellar population through time. Using the EAGLE cosmological simulations and models of protoplanetary formation, our modeling predicts an ISO population with a bimodal distribution in their water mass fraction: objects formed in low-metallicity, typically older, systems have a higher water fraction than their counterparts formed in high-metallicity protoplanetary disks, and these water-rich objects comprise the majority of the population. Both detected ISOs seem to belong to the lower water fraction population; these results suggest they come from recently formed systems. We show that the population of ISOs in galaxies with different star formation histories will have different proportions of objects with high and low water fractions. This work suggests that it is possible that the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time will detect a large enough population of ISOs to place useful constraints on models of protoplanetary disks, as well as galactic structure and evolution.

Abstract:

We present Galaxy Zoo DECaLS: detailed visual morphological classifications for Dark Energy Camera Legacy Survey images of galaxies within the SDSS DR8 footprint. Deeper DECaLS images (r = 23.6 versus r = 22.2 from SDSS) reveal spiral arms, weak bars, and tidal features not previously visible in SDSS imaging. To best exploit the greater depth of DECaLS images, volunteers select from a new set of answers designed to improve our sensitivity to mergers and bars. Galaxy Zoo volunteers provide 7.5 million individual classifications over 314 000 galaxies. 140 000 galaxies receive at least 30 classifications, sufficient to accurately measure detailed morphology like bars, and the remainder receive approximately 5. All classifications are used to train an ensemble of Bayesian convolutional neural networks (a state-of-the-art deep learning method) to predict posteriors for the detailed morphology of all 314 000 galaxies. We use active learning to focus our volunteer effort on the galaxies which, if labelled, would be most informative for training our ensemble. When measured against confident volunteer classifications, the trained networks are approximately 99 per cent accurate on every question. Morphology is a fundamental feature of every galaxy; our human and machine classifications are an accurate and detailed resource for understanding how galaxies evolve.