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Retrievals of Dayside Emission Spectra: Trends in Chemistry

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This research focuses on the retrieval of dayside emission spectra from various exoplanets, using advanced techniques such as Optimal Estimation and Markov Chain Monte Carlo (MCMC). The study identifies trends in atmospheric compositions, particularly examining equilibrium versus disequilibrium conditions in planetary atmospheres. Utilizing data from Spitzer, including broadband and IRS observations, it highlights the importance of spectroscopic measurements for understanding gas compositions and temperatures. The findings stress the need for dedicated space-based instruments for further analysis.

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Retrievals of Dayside Emission Spectra: Trends in Chemistry

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  1. Retrievals of Dayside Emission Spectra: Trends in Chemistry Michael Line, Aaron Wolf, Xi Zhang, Yuk Yung Caltech

  2. Ions Photochemistry Vertically Mixed CO CH4 quenched CH4 CO quenched Thermo. Eq.

  3. H2O + CH4 = CO + 3H2

  4. Synthetic Study Spitzer Broadband+IRS+NICMOS Spitzer Broadband FINESSE S/N~3.5 Δλ=0.0075 μm

  5. MCMC Optimal Est.

  6. GJ436 Line et al. 2011 HD189733 Moses et al. 2011 WASP12 Kopparapu et al. 2012

  7. GJ436 Line et al. 2011 HD189733 Moses et al. 2011 TRES2 WASP12 Kopparapu et al. 2012 HD189733 WASP19 WASP12 HD149026 GJ436

  8. MCMC OPT. EST. Dis-eq. Models GJ436 Line et al. 2011 HD189733 Moses et al. 2011 TRES2 WASP12 Kopparapu et al. 2012 HD189733 WASP19 WASP12 HD149026 GJ436

  9. Conclusions • Opt est and MCMC agree for “quality” data • Most planets seem out of equilibrium (to within “1-sigma”) • Errors large on current gas estimations • Need dedicated space based spectroscopic instrument • Can maybe constrain Kzz

  10. Goals • Look at the ensemble of planetary atmospheres. Indentify trends in composition—equilibrium vs. disequilibrium • First must robustly determine temperatures and compositions of exoplanet atmosphere

  11. Two Bayesian Retrieval Approaches Optimal Estimation (Lee et al. 2011 , Line et al. 2012) Markov Chain Monte Carlo (Madhusudhan et al. 2011 , Benneke & Seager 2012) That Parameter That Parameter This Parameter • Levenberg-Marquardt to find best solution • Assumes Gaussian posterior • - Fast—not slowed down by additional parameters or more sophisticated forward models • Randomly explore’s all of parameter space • - Accounts for non-Gaussian posteriors • - Slow—many parameters and more sophisticated forward models unwieldy This Parameter Forward Model: [T, fH2O, fCH4, fCO ,fCO2] Guillot 2010 [γv1, γv2, κIR, α, β]

  12. Synthetic Study Spitzer Broadband+IRS+NICMOS Spitzer Broadband FINESSE MCMC Opt. Est. True

  13. Synthetic Study Spitzer Broadband+IRS+NICMOS Spitzer Broadband FINESSE

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