Cluster Analysis of Presolar Silicon Carbide Grains: Evaluation of Their Classification and Astrophysical Implications
Asmaa Boujibar, Samantha Howell, Shuang Zhang, Grethe Hystad, Anirudh Prabhu, Nan Liu, Thomas Stephan, Shweta Narkar, Ahmed Eleish, Shaunna M. Morrison, Robert M. Hazen, Larry R. Nittler
Abstract
Abstract Cluster analysis of presolar silicon carbide grains based on literature data for 12 C/ 13 C, 14 N/ 15 N, δ 30 Si/ 28 Si, and δ 29 Si/ 28 Si including or not inferred initial 26 Al/ 27 Al data, reveals nine clusters agreeing with previously defined grain types but also highlighting new divisions. Mainstream grains reside in three clusters probably representing different parent star metallicities. One of these clusters has a compact core, with a narrow range of composition, pointing to an enhanced production of SiC grains in asymptotic giant branch (AGB) stars with a narrow range of masses and metallicities. The addition of 26 Al/ 27 Al data highlights a cluster of mainstream grains, enriched in 15 N and 26 Al, which cannot be explained by current AGB models. We defined two AB grain clusters, one with 15 N and 26 Al excesses, and the other with 14 N and smaller 26 Al excesses, in agreement with recent studies. Their definition does not use the solar N isotopic ratio as a divider, and the contour of the 26 Al-rich AB cluster identified in this study is in better agreement with core-collapse supernova models. We also found a cluster with a mixture of putative nova and AB grains, which may have formed in supernova or nova environments. X grains make up two clusters, having either strongly correlated Si isotopic ratios or deviating from the 2/3 slope line in the Si 3-isotope plot. Finally, most Y and Z grains are jointly clustered, suggesting that the previous use of 12 C/ 13 C = 100 as a divider for Y grains was arbitrary. Our results show that cluster analysis is a powerful tool to interpret the data in light of stellar evolution and nucleosynthesis modeling and highlight the need of more multi-element isotopic data for better classification.