activity
20222026
most citedDirect Formation of Planetary Embryos in Self-Gravitating Disks

23 citations · 42 across the 6 of their papers we have counts for

collaborators

7 papers

astro-ph.EP2026

From spirals to rings: dust dynamics in gravitoturbulent protoplanetary discs after late infall

Clément Baruteau, Steven Rendon Restrepo, Gaylor Wafflard-Fernandez +7

Substructures appear to be a common trait of all extended protoplanetary discs. They are found notably in discs still surrounded by ambient, possibly infalling material. In this st…

astro-ph.EP2026

Volatile depletion in rocky planets as a chemical fingerprint of hybrid accretion

Haiyang S. Wang, Anders Johansen, Ziyan Xu +7

Volatile depletion in rocky planets relative to their host stars is commonplace in both the Solar System and exoplanetary systems, yet the connections between planet formation and…

astro-ph.EP2026

Hydrodynamical Simulations of Resonant Breaking in Multi-Planet Systems via Rebound Migration During Disk Dispersal

Beibei Liu, Clément Baruteau, Zhaohuan Zhu +3

This study extends the investigation of rebound outward migration to multi-planet systems near an inner expanding disk cavity driven by stellar X-ray photoevaporation. Using 2D hyd…

astro-ph.EP2026★ 2 cited

Effects of Stellar X-ray Photoevaporation on Planetesimal Formation via the Streaming Instability

Xuchu Ying, Beibei Liu, Haifeng Yang +7

The formation of planetesimals via the streaming instability (SI) is a crucial step in planet formation, yet its triggering conditions and efficiency are highly sensitive to both d…

astro-ph.EP2024★ 12 cited

Dust-gas dynamics driven by the streaming instability with various pressure gradients

Stanley A. Baronett, Chao-Chin Yang, Zhaohuan Zhu

The streaming instability, a promising mechanism to drive planetesimal formation in dusty protoplanetary discs, relies on aerodynamic drag naturally induced by the background radia…

astro-ph.EP2022★ 5 cited

Particle clustering in turbulence: Prediction of spatial and statistical properties with deep learning

Yan-Mong Chan, Natascha Manger, Yin Li +4

We investigate the utility of deep learning for modeling the clustering of particles that are aerodynamically coupled to turbulent fluids. Using a Lagrangian particle module within…