Interstellar comet 3I/ATLAS shows water D/H > 6.6×10^{-3}, over 30 times higher than typical Solar System comets, indicating formation under colder conditions in another planetary system.
Deuterium Fractionation: the Ariadne's Thread from the Pre-collapse Phase to Meteorites and Comets today
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
The Solar System formed about 4.6 billion years ago from a condensation of matter inside a molecular cloud. Trying to reconstruct what happened is the goal of this chapter. For that, we put together our understanding of Galactic objects that will eventually form new suns and planetary systems, with our knowledge on comets, meteorites and small bodies of the Solar System today. Our specific tool is the molecular deuteration, namely the amount of deuterium with respect to hydrogen in molecules. This is the Ariadne's thread that helps us to find the way out from a labyrinth of possible histories of our Solar System. The chapter reviews the observations and theories of the deuterium fractionation in pre-stellar cores, protostars, protoplanetary disks, comets, interplanetary dust particles and meteorites and links them together trying to build up a coherent picture of the history of the Solar System formation. We emphasise the interdisciplinary nature of the chapter, which gathers together researchers from different communities with the common goal of understanding the Solar System history.
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The paper proposes the iSEEDs project to integrate machine learning with astrochemistry for extracting physical conditions and molecular abundances from protostellar disk datasets.
citing papers explorer
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Water D/H in 3I/ATLAS as a Probe of Formation Conditions in Another Planetary System
Interstellar comet 3I/ATLAS shows water D/H > 6.6×10^{-3}, over 30 times higher than typical Solar System comets, indicating formation under colder conditions in another planetary system.
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Astrochemical Study of Early Embedded Disks
The paper proposes the iSEEDs project to integrate machine learning with astrochemistry for extracting physical conditions and molecular abundances from protostellar disk datasets.