Interior Structure of Low-Mass Exoplanets

Baumeister, Philipp; Tosi, Nicola

doi:10.1007/978-3-642-27833-4_5608-1

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…For instance, Baumeister et al (2020) found that adding the Love number as input to interior structure models significantly decreases the degeneracy of the possible interior structure. Furthermore, simulations of the interior structure of low-mass planets by Baumeister & Tosi (2023) found that a Love number precision of 10σ can constrain the core and mantle size of an Earth analog to ∼13% of its true value. Here we show how the retrieved value of h 2 f from the transit and phase curve fits of the different instruments can be used to place constraints on the core mass fraction M c /M of the planet.…”

Section: Constraining the Core Mass From H 2fmentioning

confidence: 99%

Effects of tidal deformation on planetary phase curves

Akinsanmi,

Lendl,

Boué

et al. 2024

A&A

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With the continuous improvement in the precision of exoplanet observations, it has become feasible to probe for subtle effects that can enable a more comprehensive characterization of exoplanets. A notable example is the tidal deformation of ultra-hot Jupiters by their host stars, whose detection can provide valuable insights into the planetary interior structure. In this work we extend previous research on modeling deformation in transit light curves by proposing a straightforward approach to account for tidal deformation in phase curve observations. The planetary shape is modeled as a function of the second fluid Love number for radial deformation For a planet in hydrostatic equilibrium provides constraints on the interior structure of the planet. We show that the effect of tidal deformation manifests across the full orbit of the planet as its projected area varies with phase, thereby allowing us to better probe the planet's shape in phase curves than in transits. Comparing the effects and detectability of deformation by different space-based instruments ( and we find that the effect of deformation is more prominent in infrared observations where the phase curve amplitude is the largest. A single phase curve observation of a deformed planet, such as WASP-12\,b, can allow up to a 17sigma measurement of compared to 4sigma from transit-only observation. This high-precision measurement can constrain the core mass of the planet to within 19<!PCT!> of the total mass, thus providing unprecedented constraints on the interior structure. Due to the lower phase curve amplitudes in the optical, the other instruments provide $ precision on depending on the number of phase curves observed. We also find that detecting deformation from infrared phase curves is less affected by uncertainty in limb darkening, unlike detection in transits. Finally, the assumption of sphericity when analyzing the phase curve of deformed planets can lead to biases in several system parameters (radius, dayside and nightside temperatures, and hotspot offset, among others), thereby significantly limiting their accurate characterization.

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Section: Constraining the Core Mass From H 2fmentioning

confidence: 99%

Effects of tidal deformation on planetary phase curves

Akinsanmi,

Lendl,

Boué

et al. 2024

A&A

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“…Previous studies have suggested deep learning approaches to characterize the interior of exoplanets by predicting the distribution of interior features given the planetary mass, radius, and several additional parameters (e.g., the fluid Love number k 2 , the effective temperature, the temperature at one bar), thus addressing the inverse problem directly (Baumeister et al 2020;Zhao & Ni 2022;Baumeister & Tosi 2023). Recently, Haldemann et al (2023) presented an approach allowing inference of both the inverse problem and the forward interior model.…”

Section: Introductionmentioning

confidence: 99%

NeuralCMS: A deep learning approach to study Jupiter’s interior

Ziv,

Galanti,

Sheffer

et al. 2024

A&A

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NASA's Juno mission provided exquisite measurements of Jupiter's gravity field that together with the Galileo entry probe atmospheric measurements constrains the interior structure of the giant planet. Inferring its interior structure range remains a challenging inverse problem requiring a computationally intensive search of combinations of various planetary properties, such as the cloud-level temperature, composition, and core features, requiring the computation of sim $10^9$ interior models. We propose an efficient deep neural network (DNN) model to generate high-precision wide-ranged interior models based on the very accurate but computationally demanding concentric MacLaurin spheroid (CMS) method. We trained a sharing-based DNN with a large set of CMS results for a four-layer interior model of Jupiter, including a dilute core, to accurately predict the gravity moments and mass, given a combination of interior features. We evaluated the performance of the trained DNN (NeuralCMS) to inspect its predictive limitations. NeuralCMS shows very good performance in predicting the gravity moments, with errors comparable with the uncertainty due to differential rotation, and a very accurate mass prediction. This allowed us to perform a broad parameter space search by computing only sim $10^4$ actual CMS interior models, resulting in a large sample of plausible interior structures, and reducing the computation time by a factor of $10^5$. Moreover, we used a DNN explainability algorithm to analyze the impact of the parameters setting the interior model on the predicted observables, providing information on their nonlinear relation.

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Interior Structure of Low-Mass Exoplanets

Cited by 2 publications

References 25 publications

Effects of tidal deformation on planetary phase curves

Effects of tidal deformation on planetary phase curves

NeuralCMS: A deep learning approach to study Jupiter’s interior

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