Earth and Planetary Astrophysics (astro-ph.EP)

Abstract: Characterizing the interior structure of exoplanets is essential for understanding their diversity, formation, and evolution. As the interior of exoplanets is inaccessible to observations, an inverse problem must be solved, where numerical structure models need to conform to observable parameters such as mass and radius. This is a highly degenerate problem whose solution often relies on computationally-expensive and time-consuming inference methods such as Markov Chain Monte Carlo. We present ExoMDN, a machine-learning model for the interior characterization of exoplanets based on Mixture Density Networks (MDN). The model is trained on a large dataset of more than 5.6 million synthetic planets below 25 Earth masses consisting of an iron core, a silicate mantle, a water and high-pressure ice layer, and a H/He atmosphere. We employ log-ratio transformations to convert the interior structure data into a form that the MDN can easily handle. Given mass, radius, and equilibrium temperature, we show that ExoMDN can deliver a full posterior distribution of mass fractions and thicknesses of each planetary layer in under a second on a standard Intel i5 CPU. Observational uncertainties can be easily accounted for through repeated predictions from within the uncertainties. We use ExoMDN to characterize the interior of 22 confirmed exoplanets with mass and radius uncertainties below 10% and 5% respectively, including the well studied GJ 1214 b, GJ 486 b, and the TRAPPIST-1 planets. We discuss the inclusion of the fluid Love number $k_2$ as an additional (potential) observable, showing how it can significantly reduce the degeneracy of interior structures. Utilizing the fast predictions of ExoMDN, we show that measuring $k_2$ with an accuracy of 10% can constrain the thickness of core and mantle of an Earth analog to $\approx13\%$ of the true values.

Abstract: Compressive strength is a key to understanding the internal structure of dust aggregates in protoplanetary disks and their resultant bodies, such as comets and asteroids in the Solar System. Previous work has modeled the compressive strength of highly-porous dust aggregates with volume filling factors lower than 0.1. However, a comprehensive understanding of the compressive strength from low ($<0.1$) to high ($>0.1$) volume filling factors is lacking. In this paper, we investigate the compressive strength of dust aggregates by using aggregate compression simulations resolving constituent grains based on JKR theory to formulate the compressive strength comprehensively. We perform a series of numerical simulations with moving periodic boundaries mimicking the compression behavior. As a result, we find that the compressive strength becomes sharply harder when the volume filling factor exceeds 0.1. We succeed in formulating the compressive strength comprehensively by taking into account the rolling motion of aggregates for low volume filling factors and the closest packing of aggregates for high volume filling factors. We also find that the dominant compression mechanisms for high volume filling factors are sliding and twisting motions, while rolling motion dominates for low volume filling factors. We confirm that our results are in good agreement with previous numerical studies. We suggest that our analytical formula is consistent with the previous experimental results if we assume the surface energy of silicate is $\simeq210\pm90\mathrm{\ mJ\ m^{-2}}$. Now, we can apply our results to properties of small compact bodies, such as comets, asteroids, and pebbles.

Abstract: Radio and near-infrared observations have observed dozens of protoplanetary disks that host spiral arm features. Numerical simulations have shown that companions may excite spiral density waves in protoplanetary disks via companion-disk interaction. However, the lack of direct observational evidence for spiral-driving companions poses challenges to current theories of companion-disk interaction. Here we report multi-epoch observations of the binary system HD 100453 with the Spectro-Polarimetric High-contrast Exoplanet REsearch (SPHERE) facility at the Very Large Telescope. By recovering the spiral features via robustly removing starlight contamination, we measure spiral motion across 4 yr to perform dynamical motion analyses. The spiral pattern motion is consistent with the orbital motion of the eccentric companion. With this first observational evidence of a companion driving a spiral arm among protoplanetary disks, we directly and dynamically confirm the long-standing theory on the origin of spiral features in protoplanetary disks. With the pattern motion of companion-driven spirals being independent of companion mass, here we establish a feasible way of searching for hidden spiral-arm-driving planets that are beyond the detection of existing ground-based high-contrast imagers.