Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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.

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The categories of this paper are: eess
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The categories of this paper are: adap-org
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
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The categories of this paper are: nucl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: econ
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-fin
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The categories of this paper are: gr-qc
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The categories of this paper are: stat
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The categories of this paper are: hep-lat
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The categories of this paper are: patt-sol
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The categories of this paper are: chao-dyn
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The categories of this paper are: ao-sci
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The categories of this paper are: comp-gas