Information Content of Exoplanetary Transit Spectra: An Initial Look

Line, Michael R.; Zhang, Xi; Vasisht, Gautam; Natraj, Vijay; Chen, Pin; Yung, Yuk L.

doi:10.1088/0004-637x/749/1/93

Cited by 138 publications

(191 citation statements)

References 43 publications

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“…Bottom left: best fit by Lee et al (2012). Bottom right: best solutions retrieved by Line et al (2012) At zero-order approximation, the emitted component of the planetary contribution can be estimated by blackbody curves at the stellar and planetary temperatures (T * and T p ):…”

Section: Fig 7amentioning

confidence: 99%

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Spectroscopy of planetary atmospheres in our Galaxy

Tinetti

Encrenaz

Coustenis

2013

Astron Astrophys Rev

128

111

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About 20 years after the discovery of the first extrasolar planet, the number of planets known has grown by three orders of magnitude, and continues to increase at neck breaking pace. For most of these planets we have little information, except for the fact that they exist and possess an address in our Galaxy. For about one third of them, we know how much they weigh, their size and their orbital parameters. For less than 20, we start to have some clues about their atmospheric temperature and composition. How do we make progress from here?We are still far from the completion of a hypothetical Hertzsprung-Russell diagram for planets comparable to what we have for stars, and today we do not even know whether such classification will ever be possible or even meaningful for planetary objects. But one thing is clear: planetary parameters such as mass, radius and temperature alone do not explain the diversity revealed by current observations. The chemical composition of these planets is needed to trace back their formation history and evolution, as happened for the planets in our Solar System. As in situ measurements are and will remain off-limits for exoplanets, to study their chemical composition we will have to rely on remote sensing spectroscopic observations of their gaseous envelopes.

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Section: Fig 7amentioning

confidence: 99%

“…Top right: best solutions retrieved by Griffith and Tinetti (Griffith and Tinetti 2010;Swain et al 2009b). Bottom: best solutions retrieved by Madhusudhan and Seager (2009) 2009b; Madhusudhan and Seager 2009;Lee et al 2012;Line et al 2012). In Fig.…”

Section: Fig 7amentioning

confidence: 99%

Spectroscopy of planetary atmospheres in our Galaxy

Tinetti

Encrenaz

Coustenis

2013

Astron Astrophys Rev

128

111

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“…For example, the photolytic products hydrazine (N 2 H 4 ) and diphosphene (P 2 H 4 ) are expected to be the principal contributors to the hazes in the upper tropospheres of Jupiter and Saturn, although these have never been spectrally identified by remote sensing. Furthermore the disequilibrium models have proved incapable of reproducing the high CO 2 abundances inferred on several EGPs [23,[74][75][76], owing to either missing species in the spectral retrievals or missing photochemical pathways in the model.…”

Section: (E) Photochemical Disequilibriummentioning

confidence: 99%

“…optimal estimation [74,75,[82][83][84][85]). In the latter cases, the intention is to remove bias to any particular assumptions (C/O ratio, solar composition, equilibrium versus disequilibrium) and investigate the possibilities supported by the measurements in as large a parameter space as possible.…”

Section: (A) Degeneracies In Spectral Retrievalmentioning

confidence: 99%

Exploring the diversity of Jupiter-class planets

Fletcher

Irwin

Barstow

et al. 2014

Phil. Trans. R. Soc. A.

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Of the 900+ confirmed exoplanets discovered since 1995 for which we have constraints on their mass (i.e. not including Kepler candidates), 75% have masses larger than Saturn (0.3 M J ), 53% are more massive than Jupiter and 67% are within 1 AU of their host stars. When Kepler candidates are included, Neptunesized giant planets could form the majority of the planetary population. And yet the term 'hot Jupiter' fails to account for the incredible diversity of this class of astrophysical object, which exists on a continuum of giant planets from the cool jovians of our own Solar System to the highly irradiated, tidally locked hot roasters. We review theoretical expectations for the temperatures, molecular composition and cloud properties of hydrogen-dominated Jupiterclass objects under a variety of different conditions. We discuss the classification schemes for these Jupiter-class planets proposed to date, including the implications for our own Solar System giant planets and the pitfalls associated with compositional classification at this early stage of exoplanetary spectroscopy. We discuss the range of planetary types described by previous authors, accounting for (i) thermochemical equilibrium expectations for cloud condensation and favoured chemical stability fields; (ii) the metallicity and formation mechanism for these giant planets; (iii) the importance of

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“…The atmospheric retrieval of exoplanetary emission/transmission spectra is a complex undertaking (e.g., Madhusudhan & Seager 2009;Lee et al 2011b;Line et al 2012;Benneke & Seager 2013;Griffith 2014;Waldmann et al 2015aWaldmann et al , 2015b. Here, retrieval parameter dimensionality becomes an important factor to consider and, though desirable, most times allowing for all known atmospheric species to be fitted is too computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

Dreaming of Atmospheres

Waldmann

2016

ApJ

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Here, we introduce the RobERt(Robotic Exoplanet Recognition) algorithm for the classification of exoplanetary emission spectra. Spectral retrieval of exoplanetary atmospheres frequently requires the preselection of molecular/ atomic opacities to be defined by the user. In the era of open-source, automated, and self-sufficient retrieval algorithms, manual input should be avoided. User dependent input could, in worst-case scenarios, lead to incomplete models and biases in the retrieval. The RobERtalgorithm is based on deep-belief neural (DBN) networks trained to accurately recognize molecular signatures for a wide range of planets, atmospheric thermal profiles, and compositions. Reconstructions of the learned features, also referred to as the "dreams" of the network, indicate good convergence and an accurate representation of molecular features in the DBN. Using these deep neural networks, we work toward retrieval algorithms that themselves understand the nature of the observed spectra, are able to learn from current and past data, and make sensible qualitative preselections of atmospheric opacities to be used for the quantitative stage of the retrieval process.

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Information Content of Exoplanetary Transit Spectra: An Initial Look

Cited by 138 publications

References 43 publications

Spectroscopy of planetary atmospheres in our Galaxy

Spectroscopy of planetary atmospheres in our Galaxy

Exploring the diversity of Jupiter-class planets

Dreaming of Atmospheres

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