Extremely Large Telescope

Abstract:

Abstract We present high-resolution dayside thermal emission observations of the exoplanet WASP-18 b using IGRINS on Gemini South. We remove stellar and telluric signatures using standard algorithms, and we extract the planet signal via cross-correlation with model spectra. We detect the atmosphere of WASP-18 b at a signal-to-noise ratio (S/N) of 5.9 using a full chemistry model, measure H 2 O (S/N = 3.3), CO (S/N = 4.0), and OH (S/N = 4.8) individually, and confirm previous claims of a thermal inversion layer. The three species are confidently detected (>4 σ ) with a Bayesian inference framework, which we also use to retrieve abundance, temperature, and velocity information. For this ultrahot Jupiter (UHJ), thermal dissociation processes likely play an important role. Retrieving abundances constant with altitude and allowing the temperature–pressure profile to adjust freely results in a moderately super-stellar carbon-to-oxygen ratio (C/O = 0.75 − 0.17 + 0.14 ) and metallicity ([M/H] = 1.03 − 1.01 + 0.65 ). Accounting for undetectable oxygen produced by thermal dissociation leads to C/O = 0.45 − 0.10 + 0.08 and [M/H] = 1.17 − 1.01 + 0.66 . A retrieval that assumes radiative–convective–thermochemical equilibrium and naturally accounts for thermal dissociation constrains C/O < 0.34 (2 σ ) and [M/H] = 0.48 − 0.29 + 0.33 , in line with the chemistry of the parent star. Looking at the velocity information, we see a tantalizing signature of different Doppler shifts at the level of a few kilometers per second for different molecules, which might probe dynamics as a function of altitude and/or location on the planet disk. Our results demonstrate that ground-based, high-resolution spectroscopy at infrared wavelengths can provide meaningful constraints on the compositions and climate of highly irradiated planets. This work also elucidates potential pitfalls with commonly employed retrieval assumptions when applied to the spectra of UHJs.

Abstract:

We present the first Bayesian method for tomographic decomposition of the plane-of-sky orientation of the magnetic field with the use of stellar polarimetry and distance. This standalone tomographic inversion method presents an important step forward in reconstructing the magnetized interstellar medium (ISM) in three dimensions within dusty regions. We develop a model in which the polarization signal from the magnetized and dusty ISM is described by thin layers at various distances, a working assumption which should be satisfied in small-angular circular apertures. Our modeling makes it possible to infer the mean polarization (amplitude and orientation) induced by individual dusty clouds and to account for the turbulence-induced scatter in a generic way. We present a likelihood function that explicitly accounts for uncertainties in polarization and parallax. We develop a framework for reconstructing the magnetized ISM through the maximization of the log-likelihood using a nested sampling method. We test our Bayesian inversion method on mock data, representative of the high Galactic latitude sky, taking into account realistic uncertainties from Gaia and as expected for the optical polarization survey PASIPHAE according to the currently planned observing strategy. We demonstrate that our method is effective at recovering the cloud properties as soon as the polarization induced by a cloud to its background stars is higher than -0.1% for the adopted survey exposure time and level of systematic uncertainty. The larger the induced polarization is, the better the method s performance, and the lower the number of required stars. Our method makes it possible to recover not only the mean polarization properties but also to characterize the intrinsic scatter, thus creating new ways to characterize ISM turbulence and the magnetic field strength. Finally, we apply our method to an existing data set of starlight polarization with known line-of-sight decomposition, demonstrating agreement with previous results and an improved quantification of uncertainties in cloud properties.