2022ApJS..258...31K -
Astrophys. J., Suppl. Ser., 258, 31-31 (2022/February-1)
Autodifferentiable Spectrum Model for High-dispersion Characterization of Exoplanets and Brown Dwarfs.
KAWAHARA H., KAWASHIMA Y., MASUDA K., CROSSFIELD I.J.M., PANNIER E. and VAN DEN BEKEROM D.
Abstract (from CDS):
We present an autodifferentiable spectral modeling of exoplanets and brown dwarfs. This model enables a fully Bayesian inference of the high-dispersion data to fit the ab initio line-by-line spectral computation to the observed spectrum by combining it with the Hamiltonian Monte Carlo in recent probabilistic programming languages. An open-source code, ExoJAX (
https://github.com/HajimeKawahara/exojax), developed in this study, was written in Python using the GPU/TPU compatible package for automatic differentiation and accelerated linear algebra, JAX. We validated the model by comparing it with existing opacity calculators and a radiative transfer code and found reasonable agreements for the output. As a demonstration, we analyzed the high-dispersion spectrum of a nearby brown dwarf, Luhman 16 A, and found that a model including water, carbon monoxide, and H
2/He collision-induced absorption was well fitted to the observed spectrum (R = 10
5 and 2.28-2.30 µm). As a result, we found that {T}
{0}={1295}{-32}^{+35} K at 1 bar and C/O = 0.62 ± 0.03, which is slightly higher than the solar value. This work demonstrates the potential of a full Bayesian analysis of brown dwarfs and exoplanets as observed by high-dispersion spectrographs and also directly imaged exoplanets as observed by high-dispersion coronagraphy.
Abstract Copyright:
© 2022. The Author(s). Published by the American Astronomical Society.
Journal keyword(s):
Exoplanet atmospheres - High resolution spectroscopy - Brown dwarfs - Markov chain Monte Carlo
Simbad objects:
6
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