JOURNAL ARTICLE
N-body simulation of planetary formation through pebble accretion in a radially structured protoplanetary disk.
Published In: Publications of the Astronomical Society of Japan, 2023, v. 75, n. 5. P. 951 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Jinno, Tenri; Saitoh, Takayuki R; Ishigaki, Yota; Makino, Junichiro 3 of 3
Abstract
This article focuses on investigating terrestrial planet formation in radially structured protoplanetary disks, particularly at the inner dead zone boundary (IDB) around 0.6 astronomical units (au) from the central star. Using a protoplanetary disk model that incorporates turbulent and non-turbulent (dead) zones, the study simulates dust growth and inward migration leading to pebble accumulation at the IDB, where pressure bumps trap solids. Large-scale N-body simulations with up to one million particles model pebble accretion and planetesimal growth, revealing that Earth-mass protoplanets form efficiently within ~10,000 years, with multiple planet-sized bodies emerging at regular intervals near the IDB. The results show that planet formation timescales are shorter than classical models, final planetary eccentricities remain low due to pebble accretion dominance, and outcomes are robust across variations in dust-to-gas ratios and simulation resolution, supporting the possibility of solar system-like planetary systems forming in disks with radial structures.
Additional Information
- Source:Publications of the Astronomical Society of Japan. 2023/10, Vol. 75, Issue 5, p951
- Document Type:Article
- Subject Area:Astronomy and Astrophysics
- Publication Date:2023
- ISSN:0004-6264
- DOI:10.1093/pasj/psad053
- Accession Number:172895862
- Copyright Statement:Copyright of Publications of the Astronomical Society of Japan is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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