JOURNAL ARTICLE
Impacts of stellar wind and supernovae on star cluster formation: Origins of extremely high N/O ratios and multiple stellar populations.
Published In: Publications of the Astronomical Society of Japan, 2024, v. 76, n. 5. P. 1122 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Fukushima, Hajime; Yajima, Hidenobu 3 of 3
Abstract
This article focuses on the metal enrichment processes from stellar winds and supernovae (SNe) in low-metallicity clouds during massive star cluster formation, investigated through three-dimensional radiation hydrodynamics simulations. It finds that nitrogen enrichment, primarily from Wolf–Rayet (WR) stellar winds, accumulates in sufficiently massive (≥10^6 solar masses) and compact star clusters before SNe occur, producing nitrogen-enhanced gas and subsequent star formation with elevated nitrogen-to-oxygen ([N/O]) ratios comparable to those observed in certain high-redshift galaxies. The study shows that if star formation lasts longer than the WR phase but shorter than the SN timescale (~10 Myr), a significant fraction of stars form from nitrogen-enriched gas, whereas longer durations lead to oxygen enrichment from SNe and lower [N/O] ratios. These results suggest that massive, dense star-forming clumps observed in early galaxies by JWST are plausible sites for globular cluster formation with multiple stellar populations, linking early starburst activity to observed chemical abundance patterns.
Additional Information
- Source:Publications of the Astronomical Society of Japan. 2024/10, Vol. 76, Issue 5, p1122
- Document Type:Article
- Subject Area:Astronomy and Astrophysics
- Publication Date:2024
- ISSN:0004-6264
- DOI:10.1093/pasj/psae074
- Accession Number:180267107
- 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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