Retrievals of Dayside Emission Spectra: Trends in Chemistry

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

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Retrievals of Dayside Emission Spectra: Trends in Chemistry. Michael Line, Aaron Wolf, Xi Zhang, Yuk Yung Caltech. Ions. Photochemistry. Vertically Mixed. CO . CH 4 quenched. CH 4. CO quenched. Thermo. Eq. H 2 O + CH 4 = CO + 3H 2. Synthetic Study. Spitzer Broadband+IRS+NICMOS. - PowerPoint PPT Presentation

Transcript of Retrievals of Dayside Emission Spectra: Trends in Chemistry

Retrievals of Dayside Emission Spectra: Trends in Chemistry

Michael Line, Aaron Wolf, Xi Zhang,Yuk YungCaltech

Thermo. Eq.

Vertically Mixed

Photochemistry

Ions

CO

CH4

CO quenched

CH4 quenched

fH2O fCH4fCO fH2

3 P 2=Keq

H2O + CH4 = CO + 3H2

Synthetic Study

S/N~3.5Δλ=0.0075 μm

Spitzer Broadband Spitzer Broadband+IRS+NICMOS

FINESSE

MCMC

Optimal Est.

HD189733Moses et al. 2011

WASP12Kopparapu et al. 2012

GJ436Line et al. 2011

GJ436

HD189733

TRES2

HD149026

WASP19 WASP12

HD189733Moses et al. 2011

WASP12Kopparapu et al. 2012

GJ436Line et al. 2011

GJ436

HD189733

TRES2

HD149026

WASP19 WASP12

MCMC

OPT. EST.

Dis-eq. Models

HD189733Moses et al. 2011

WASP12Kopparapu et al. 2012

GJ436Line et al. 2011

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

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

Two Bayesian Retrieval Approaches

P(T,f |Data)∝ P(Data |T,f) × P(T,f)Optimal Estimation

(Lee et al. 2011 , Line et al. 2012)

This Parameter

That

Par

amet

er

χ 2

-Levenberg-Marquardt to find best solution

-Assumes Gaussian posterior

- Fast—not slowed down by additional parameters or more sophisticated forward models

This Parameter

That

Par

amet

er

χ 2

- Randomly explore’s all of parameter space

- Accounts for non-Gaussian posteriors

- Slow—many parameters and more sophisticated forward models unwieldy

Markov Chain Monte Carlo(Madhusudhan et al. 2011 , Benneke & Seager 2012)

Forward Model: [T, fH2O, fCH4, fCO ,fCO2]

Guillot 2010 [γv1, γv2, κIR, α, β]

Synthetic StudySpitzer Broadband Spitzer Broadband+IRS+NICMOS

FINESSE

MCMCOpt. Est.True

Synthetic StudySpitzer Broadband Spitzer Broadband+IRS+NICMOS

FINESSE