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Harvard ADS: Differentiable Modeling of Planet and Substellar Atmosphere: High-Resolution Emission, Transmission, and Reflection Spectroscopy with ExoJAX2

Paper abstract: Modeling based on differentiable programming holds great promise for astronomy, as it can employ techniques such as Hamiltonian Monte Carlo, gradient-based optimization, and other machine learning techniques. This new programming paradigm has motivated us to develop the first auto-differentiable spectrum model of exoplanets and brown dwarfs, ExoJAX (Kawahara et al. 2022). ExoJAX is designed to directly calculate cross-sections as functions of temperature and pressure, rather than interpolating tabulated data, to minimize errors in high-dispersion spectra modeling. However, its application was primarily proof-of-concept and limited to narrowband high-dispersion emission spectroscopy. In this paper, we have enhanced the differentiable opacity calculation using a new fast and memory-efficient algorithm, and have developed differentiable radiative transfer schemes, including emission, transmission, and reflection spectroscopy. These enhancements significantly expand the range of applications, as demonstrated through actual atmospheric retrievals: high-dispersion emission spectra of the brown dwarf GL229 B, medium-dispersion transmission spectra of the hot Saturn WASP-39 b from JWST, and high-dispersion reflection spectra of Jupiter. We obtained a C/O ratio for GL229 B consistent with its host star, constrained WASP-39 b's radial velocity from molecular fine structures at original resolution (R ~ 3,000), and estimated Jupiter's metallicity consistent with previous studies.