Towards multi-dimensional analysis of transmission spectroscopy. Part II: Day-night induced biases in retrievals from hot to ultra-hot Jupiters
Hot Jupiters 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 hetereogeneities between the day and the night side of their atmosphere. For the hottest ones, 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 ultra hot Jupiters. Here, we quantify the effective temperature domain over which these biases are present. We use a set of 12 simulations of typical Hot Jupiters from Teq = 1000 K to Teq = 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 are then analyzed using the 1D TauREx retrieval code. We find that for JWST-like data, accounting for non-isothermal vertical temperature profiles is required over the whole temperature range. We further find that 1D retrieval codes start to estimate wrong parameter values for planets with equilibrium temperatures greater than 1400 K if there are absorbers in the visible able to create a hot stratosphere. The high temperatures at low pressures indeed entail a thermal dissociation of species which creates a strong chemical day-night dichotomy. As a by-product, we demonstrate that when using synthetic observations to assess the detectability of a given feature or process using a Bayesian framework, it is valid to use non-randomized input data, as long as the anticipated observational uncertainties are correctly taken into account.