Hot Jupiters (HJs) are very good targets for transmission spectroscopy analysis. Their atmospheres have a large scale height, implying a high signal-to-noise ratio. As these planets orbit close to their stars, they often present strong thermal and chemical heterogeneities between the day- and nightside of their atmosphere. For the hottest of these planets, the thermal dissociation of several species occurs in their atmospheres, which leads to a stronger chemical dichotomy between the two hemispheres. It has already been shown that the current retrieval algorithms, which are using 1D forward models, find biased molecular abundances in ultrahot Jupiters. Here, we quantify the effective temperature domain over which these biases are present. We used a set of 12 simulations of typical HJs from T-eq = 1000 K to T-eq = 2100 K performed with the substellar and planetary atmospheric radiation and circulation global climate model and generate transmission spectra that fully account for the 3D structure of the atmosphere with Pytmosph3R. These spectra were then analyzed using the 1D TauREx retrieval code. We find that for James Webb Space Telescope like data, accounting for nonisothermal vertical temperature profiles is required over the whole temperature range. We further find that 1D retrieval codes start to estimate incorrect parameter values for planets with equilibrium temperatures greater than 1400 K if there are absorbers in the visible (such as TiO and VO, e.g.) that are able to create a hot stratosphere. The high temperatures at low pressures indeed entail a thermal dissociation of species that creates a strong chemical day-night dichotomy. As a byproduct, we demonstrate that when synthetic observations are used to assess the detectability of a given feature or process using a Bayesian framework (e.g., by comparing the Bayesian evidence of retrievals with different model assumptions), it is valid to use nonrandomized input data as long as the anticipated observational uncertainties are correctly taken into account.
Toward a multidimensional analysis of transmission spectroscopy. II. Day-night-induced biases in retrievals from hot to ultrahot Jupiters
Zingales, Tiziano;
2022
Abstract
Hot Jupiters (HJs) are very good targets for transmission spectroscopy analysis. Their atmospheres have a large scale height, implying a high signal-to-noise ratio. As these planets orbit close to their stars, they often present strong thermal and chemical heterogeneities between the day- and nightside of their atmosphere. For the hottest of these planets, the thermal dissociation of several species occurs in their atmospheres, which leads to a stronger chemical dichotomy between the two hemispheres. It has already been shown that the current retrieval algorithms, which are using 1D forward models, find biased molecular abundances in ultrahot Jupiters. Here, we quantify the effective temperature domain over which these biases are present. We used a set of 12 simulations of typical HJs from T-eq = 1000 K to T-eq = 2100 K performed with the substellar and planetary atmospheric radiation and circulation global climate model and generate transmission spectra that fully account for the 3D structure of the atmosphere with Pytmosph3R. These spectra were then analyzed using the 1D TauREx retrieval code. We find that for James Webb Space Telescope like data, accounting for nonisothermal vertical temperature profiles is required over the whole temperature range. We further find that 1D retrieval codes start to estimate incorrect parameter values for planets with equilibrium temperatures greater than 1400 K if there are absorbers in the visible (such as TiO and VO, e.g.) that are able to create a hot stratosphere. The high temperatures at low pressures indeed entail a thermal dissociation of species that creates a strong chemical day-night dichotomy. As a byproduct, we demonstrate that when synthetic observations are used to assess the detectability of a given feature or process using a Bayesian framework (e.g., by comparing the Bayesian evidence of retrievals with different model assumptions), it is valid to use nonrandomized input data as long as the anticipated observational uncertainties are correctly taken into account.
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