TL;DR
This paper introduces an auto-differentiable spectral model for exoplanets and brown dwarfs, enabling Bayesian inference of high-dispersion spectra with improved accuracy and efficiency, demonstrated through analysis of a brown dwarf spectrum.
Contribution
The study develops an open-source, GPU-compatible Python package for auto-differentiable spectral modeling, integrating Bayesian inference with high-dispersion spectral data analysis.
Findings
Validated the model against existing opacity calculators and radiative transfer codes.
Successfully fitted the spectrum of brown dwarf Luhman 16 A, deriving temperature and C/O ratio.
Demonstrated potential for Bayesian analysis of exoplanet and brown dwarf spectra.
Abstract
We present an auto-differentiable 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, developed in this study, was written in Python using the GPU/TPU compatible package for automatic differentiation and accelerated linear algebra, JAX (Bradbury et al. 2018). We validated the model by comparing it with existing opacity calculators and a radiative transfer code and found reasonable agreements of 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 collision induced absorption was…
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