Dissemin is shutting down on January 1st, 2025

Published in

American Astronomical Society, Astrophysical Journal, 1(950), p. 8, 2023

DOI: 10.3847/1538-4357/acc8cb

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Self-consistent Models of Y Dwarf Atmospheres with Water Clouds and Disequilibrium Chemistry

Journal article published in 2023 by Brianna Lacy ORCID, Adam Burrows ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Data provided by SHERPA/RoMEO

Abstract

Abstract Y dwarfs are the coolest spectral class of brown dwarf. They have effective temperatures less than 500 K, with the coolest detection as low as ∼250 K. They make up the low-mass tail of the star formation process, and are a valuable analog to the atmospheres of giant gaseous exoplanets in a temperature range that is difficult to observe. Understanding Y dwarf atmospheric compositions and processes will thus deepen our understanding of planet and star formation and provide a stepping stone toward characterizing cool exoplanets. Their spectra are shaped predominantly by gaseous water, methane, and ammonia. At the warmer end of the Y-dwarf temperature range, spectral signatures of disequilibrium carbon monoxide have been observed. Cooler Y dwarfs could host water clouds in their atmospheres. JWST spectral observations are anticipated to provide an unprecedented level of detail for these objects, and yet published self-consistent model grids do not accurately replicate even the existing Hubble Space Telescope and ground-based observations. In this work, we present a new suite of 1D radiative-convective equilibrium models to aid in the characterization of Y-dwarf atmospheres and spectra. We compute clear, cloudy, equilibrium chemistry and disequilibrium chemistry models, providing a comprehensive suite of models in support of the impending JWST era of panchromatic Y-dwarf characterization. Comparing these models against current observations, we find that disequilibrium CH4–CO and NH3–N2 chemistry and the presence of water clouds can bring models and observations into better, though still not complete, agreement.