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Abstract:
Characterization of planetary atmospheres has been always a challenge. While the next generation of facilities, such as E-ELT, JWST, and ARIEL, will help to improve the status, the number of well-characterized exoplanet atmospheres will still be limited. Large-scale simulations could assist us by predicting the diversity of the planetary atmospheres, and pointing toward the regions on the parameter space where we have a higher chance of finding interesting targets with desired properties. We present the results of an extensive investigation, with a three-step strategy, to understand the fingerprints of physics, chemistry and dynamics of exoplanets on their atmospheric spectra. In the first step, we study the synthetic spectra of 28,224 self-consistent cloud-free models; assuming effective temperature, surface gravity, metallicity, C/O ratio of the planet, and host star's stellar type as the free parameters. We propose a new classification scheme and find a region (Methane Valley) between 800 and 1500 K, where a greater chance of CH4 detection is expected. The first robust CH4 detection on an irradiated planet places HD102195b within this region; supporting our prediction. We then investigate the fingerprints of disequilibrium chemistry on the atmospheric spectra by performing 84,672 full chemical network kinetic simulations with ChemKM. We find that the quenching pressure decreases with the effective temperature of planets, but it also varies with other atmospheric parameters. We show that the atmospheric mixing does not change the shape of the two main color-populations in the Spitzer color- maps and thus any deviation of observational points from these populations are likely due to the presence of clouds and not disequilibrium processes. However, we find some colder planets (Teff<900 K) with very low C/O ratios (<0.25) that show significant deviations; making these planets interesting cases for further investigations. We further present the results of 38,500 self- consistent cloudy models to demonstrate how this picture changes when the radiative feedback of clouds is included in the models.
