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Abstract:

Magnetic fields are a key factor in the evolution of planets and their atmospheres, but they are still poorly constrained for exoplanets owing to limited direct observations. Ultra-hot Jupiters provide a new avenue to probe magnetic effects, as the circulation of their highly ionized atmospheres could be directly sensitive to the atmospheric magnetic field. However, it remains unclear whether the impact of these magnetic effects can be observed directly and used to constrain the magnetic field strength. With high spectral resolution observations targeting the planetary iron lines, we measure the Doppler shift and thus the wind speed of seven transiting ultra-hot Jupiters. Here we find a clear decrease of wind speed with increasing planetary temperature, which is a trend inconsistent with purely hydrodynamic mechanisms but naturally reproduced by magnetic drag. From this relationship, we estimate the possible strength of magnetic fields of hot giant planets to at most a few gauss, which is comparable with the Jovian equatorial field. Our results support the idea that magnetic fields affect the atmospheric circulation of ultra-hot Jupiters and could provide a crucial benchmark for scaling laws used to predict magnetic fields in exoplanets, from hot Jupiters to rocky Earths, with additional implications for future direct observations.

Abstract:

Hot Jupiters have temperature gradients of several hundreds of degrees between their permanent daysides and nightsides. Such a strong gradient creates winds with speeds of the order of kilometres per second, which advect chemical species over the whole planet. When this transport is faster than the time needed for chemical species to react, it holds back the chemical equilibration of the atmospheric carbon reservoir, which would otherwise transition from CO on the dayside to CH4 on the nightside. Direct evidence of this process has remained elusive so far, as it is often degenerate with other atmospheric processes, such as vertical mixing or non-stellar elemental abundances. Here we present observational evidence for such a fast day-to-night horizontal transport of chemical species by observing the full 18-h orbit of the exoplanet NGTS-10A b with the JWST/NIRSpec instrument. We show that the carbon chemistry is dominated by CO in both the dayside and the nightside of the planet, with a strong depletion of CH4 on the nightside compared with expectations from chemical equilibrium. By measuring the atmospheric abundances of all the main carbon and oxygen molecules, we further demonstrate that the lack of CH4 on the planetary nightside cannot be attributed to non-solar elemental abundances or to vertical mixing mechanisms and must, therefore, be due to fast horizontal transport. Our study shows the fundamental role that atmospheric transport plays in shaping the distribution of chemical species on exoplanet atmospheres.

Abstract:

The elemental compositions of exoplanets encode information about their formation environments and internal structures. While volatile ratios such as carbon-to-oxygen (C/O) are used to trace formation location, the rock-forming elements-magnesium (Mg), silicon (Si), and iron (Fe)-govern interior mineralogy and are commonly assumed to reflect the host star's abundances. Yet this assumption remains largely untested. Ultra-hot Jupiters, gas-giant exoplanets with dayside temperatures above 3000 K, provide rare access to refractory elements that remain gaseous. Here we present high-resolution thermal emission spectroscopy of the exoplanet WASP-189b ( Teq=3354-34+27 K) obtained with the Immersion Grating Infrared Spectrometer (IGRINS) on Gemini South. We detect neutral iron (Fe I), magnesium (Mg I), silicon (Si I), water (H₂O), carbon monoxide (CO), and hydroxyl (OH) at signal-to-noise ratios exceeding 4, and retrieve their elemental abundances. We show that the Mg/Si, Fe/Mg, and Si/Fe ratios are consistent with stellar values, while the refractory-to-volatile ratio is enhanced by roughly a factor of 2. These findings demonstrate that giant-planet atmospheres can preserve stellar-like rock-forming ratios, providing an empirical validation of the stellar-proxy assumption that underpins planetary composition and formation models across exoplanet systems.

Abstract:

Inferring the properties of transiting exoplanet atmospheres relies on comparing models to spectroscopic observations. Atmosphere models, however, make a range of assumptions, from one-dimensional (1D, varying with altitude) radiative-convective equilibrium (RCE) to three-dimensional (3D) global circulation models (GCMs). The goal of this investigation is to determine the causes of differences in dayside thermal emission spectra resulting from 3D-GCMs (using SPARC/MITgcm) and 1D-RCE models (using ScCHIMERA). We conduct a one-to-one comparison of 1D-RCE models and 3D-GCMs with the same outgoing bolometric thermal flux over a grid of equilibrium temperatures, gravities, metallicities, and rotation periods. Each 1D-RCE model assumes heat redistribution in the planet’s atmosphere consistent with that in the corresponding 3D-GCM’s photosphere. Comparing corresponding models, the dayside average pressure–temperature (or PT) structures can be broken into four vertical regions, each influencing wavelength-dependent differences in their spectra. Furthermore, the dayside average 3D-GCM PTs for planets with Teq = 1400 K exhibit a temperature inversion, whereas corresponding 1D-RCE models do not. We find that spectral differences between 1D-RCE models and 3D-GCMs with the same parameters decrease for hotter planets because the spectral shapes more closely resemble blackbodies. To a lesser extent, spectral differences increase for planets with longer rotation periods because of smaller day–night temperature contrasts in the photosphere. Finally, we compare spectral differences to realistic observational uncertainties from JWST with the NIRISS SOSS, NIRSpec G395H, and MIRI long-resolution spectroscopy instrument modes. We find that 1D-RCE models and 3D-GCMs with the same parameters can produce dayside spectral differences larger than JWST’s uncertainty, potentially biasing data–model inferences.