Exoplanet atmospheres

Abstract:

Context . Wind dynamics play a pivotal role in governing transport processes within planetary atmospheres, influencing atmospheric chemistry, cloud formation, and the overall energy budget. Understanding the strength and patterns of winds is crucial for comprehensive insights into the physics of ultra-hot-Jupiter atmospheres. Current research has proposed different mechanisms that limit wind speeds in these atmospheres. Aims . This study focuses on unraveling the wind dynamics and the chemical composition in the atmosphere of the ultra-hot Jupiter TOI-1518 b. Methods . Two transit observations using the high-resolution ( R λ ∼ 85 000) optical (spectral coverage between 490 and 920 nm) spectrograph MAROON-X were obtained and analyzed to explore the chemical composition and wind dynamics using the cross-correlation techniques, global circulation models (GCMs), and atmospheric retrieval. Results . We report the detection of 14 species in the atmosphere of TOI-1518 b through cross-correlation analysis. VO was detected only with the new HyVO line list, whereas TiO was not detected. Additionally, we measured the time-varying cross-correlation trails for six different species, compared them with predictions from GCMs, and conclude that a strong drag is slowing the winds in TOI-1518 b’s atmosphere ( τ drag ≈ 10 3 −10 4 s). We find that the trails are species dependent. Fe+ favors stronger drag than Fe, which we interpret as a sign of magnetic effects being responsible for the observed strong drag. Furthermore, we show that Ca+ probes layers above the Roche lobe, leading to a qualitatively different trail than the other species. Finally, We used a retrieval analysis to further characterize the abundances of the different species detected. Our analysis is refined thanks to the updated planetary mass of 1.83 ± 0.47 M Jup we derived from new Sophie radial-velocity observations. We measure an abundance of Fe of log 10 Fe = −4.88 −0.76 +0.63 corresponding to 0.07 to 1.62 solar enrichment. For the other elements, the retrievals appear to be biased, probably due to the different K p /V sys shifts between Fe and the other elements, which we demonstrate for the case of VO.

Abstract:

The Cassini spacecraft observed Titan from 2004 to 2017, capturing key atmospheric features, including the tilt of the middle atmosphere and the formation and breakup of winter polar vortices. We analyze low spectral resolution infrared observations from Cassini’s Composite Infrared Spectrometer (CIRS), which provide excellent spatial and temporal coverage and the best horizontal spatial resolution of any of the CIRS observations. With approximately 4 times higher meridional resolution than previous studies, we map the stratospheric temperature for almost half a Titan year. We determine the evolution of Titan’s stratospheric tilt, finding that it is most constant in the inertial frame, directed 120° ± 6° west of the Titan–Sun vector at the northern spring equinox, with seasonal oscillations in the tilt magnitude between around 2 .° 5 and 8°. Using the high meridional resolution temperature field, we reveal finer details in the zonal wind and potential vorticity. In addition to the strong winter zonal jet, a weaker zonal jet in Titan’s summer hemisphere is observed, and there is a suggestion that the main winter hemisphere jet briefly splits into two. We also present the strongest evidence yet that Titan’s polar vortex is annular for part of its life cycle.

Abstract:

Determining the composition of an exoplanet atmosphere relies on the presence of detectable spectral features. The strongest spectral features, including dimethyl sulphide (DMS), look approximately Gaussian. Here, I perform a suite of Gaussian feature analyses to find any statistically significant spectral features in the recently published MIRI/LRS spectrum of K2-18b. In N. Madhusudhan et al., they claim a 3.4σ detection of spectral features compared to a flat line. In 5 out of 6 tests, I find the data preferred a flat line over a Gaussian model, with a χν2 of 1.06. When centering the Gaussian where the absorptions for DMS and DMDS peak, I find ln(B) = 1.21 in favour of the Gaussian model, with a χν2 of 0.99. With only ∼2σ in favour of Gaussian features, I conclude no strong statistical evidence for spectral features.