Anusha Kalyaan and Steven J. Desch
Astrophysical Journal 875, 43 Link to Article [DOI: 10.3847/1538-4357/ab0e6c ]
School of Earth & Space Exploration, Arizona State University, 550 E Tyler Mall Tempe, AZ 85287, USA

The snow line in a protoplanetary disk demarcates regions with H₂O ice from regions with H₂O vapor. Where a planet forms relative to this location determines how much water and other volatiles it forms with. Giant-planet formation may be triggered at the water–snow line if vapor diffuses outward and is cold-trapped beyond the snow line faster than icy particles can drift inward. In this study, we investigate the distribution of water across the snow line, considering three different radial profiles of the turbulence parameter α(r), corresponding to three different angular momentum transport mechanisms. We consider the radial transport of water vapor and icy particles by diffusion, advection, and drift. We show that even for similar values of α, the gradient of α(r) across the snow line significantly changes the snow line location, the sharpness of the volatile gradient across the snow line, and the final water/rock ratio in planetary bodies. A profile of radially decreasing α, consistent with transport by hydrodynamic instabilities plus magnetic disk winds, appears consistent with the distribution of water in the solar nebula, with monotonically increasing radial water content and a diverse population of asteroids with different water content. We argue that Σ(r) and water abundance are likely a diagnostic of α(r) and thus of the mechanism for angular momentum transport in inner disks.

Saverio Cambioni¹, Erik Asphaug¹, Alexandre Emsenhuber¹, Travis S. J. Gabriel², Roberto Furfaro³, and Stephen R. Schwartz¹
Astrophysical Journal 875, 40 Link to Article [DOI: 10.3847/1538-4357/ab0e8a ]
¹Lunar and Planetary Laboratory, University of Arizona, 1629 E. University Blvd., Tucson, AZ 85721, USA
²School of Earth and Space Exploration, Arizona State University, 781 E. Terrace Mall, Tempe, AZ 85287, USA
³Systems and Industrial Engineering Department, University of Arizona, 1127 E. James E. Rogers Way, Tucson, AZ 85721, USA

Planet formation simulations are capable of directly integrating the evolution of hundreds to thousands of planetary embryos and planetesimals as they accrete pairwise to become planets. In principle, these investigations allow us to better understand the final configuration and geochemistry of the terrestrial planets, and also to place our solar system in the context of other exosolar systems. While these simulations classically prescribe collisions to result in perfect mergers, recent computational advances have begun to allow for more complex outcomes to be implemented. Here we apply machine learning to a large but sparse database of giant impact studies, which allows us to streamline the simulations into a classifier of collision outcomes and a regressor of accretion efficiency. The classifier maps a four-dimensional (4D) parameter space (target mass, projectile-to-target mass ratio, impact velocity, impact angle) into the four major collision types: merger, graze-and-merge, hit-and-run, and disruption. The definition of the four regimes and their boundary is fully data-driven. The results do not suffer from any model assumption in the fitting. The classifier maps the structure of the parameter space and it provides insights into the outcome regimes. The regressor is a neural network that is trained to closely mimic the functional relationship between the 4D space of collision parameters, and a real-variable outcome, the mass of the largest remnant. This work is a prototype of a more complete surrogate model, that will be based on extended sets of simulations (big data), that will quickly and reliably predict specific collision outcomes for use in realistic N-body dynamical studies of planetary formation.

Benoit Côté^1,2,3,17et al. (>10)
Astrophysical Journal 875, 106 Link to Article [DOI: 10.3847/1538-4357/ab10db ]
¹Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, Hungarian Academy of Sciences, Konkoly Thege Miklos ut 15-17, H-1121 Budapest, Hungary

Probing the origin of r-process elements in the universe represents a multidisciplinary challenge. We review the observational evidence that probes the properties of r-process sites, and address them using galactic chemical evolution simulations, binary population synthesis models, and nucleosynthesis calculations. Our motivation is to define which astrophysical sites have significantly contributed to the total mass of r-process elements present in our Galaxy. We found discrepancies with the neutron star (NS–NS) merger scenario. When we assume that they are the only site, the decreasing trend of [Eu/Fe] at [Fe/H] > −1 in the disk of the Milky Way cannot be reproduced while accounting for the delay-time distribution (DTD) of coalescence times (∝t ⁻¹) derived from short gamma-ray bursts (GRBs) and population synthesis models. Steeper DTD functions (∝t ^−1.5) or power laws combined with a strong burst of mergers before the onset of supernovae (SNe) Ia can reproduce the [Eu/Fe] trend, but this scenario is inconsistent with the similar fraction of short GRBs and SNe Ia occurring in early-type galaxies, and it reduces the probability of detecting GW170817 in an early-type galaxy. One solution is to assume an additional production site of Eu that would be active in the early universe, but would fade away with increasing metallicity. If this is correct, this additional site could be responsible for roughly 50% of the Eu production in the early universe before the onset of SNe Ia. Rare classes of supernovae could be this additional r-process source, but hydrodynamic simulations still need to ensure the conditions for a robust r-process pattern.