The Astrophysical Journal Letters (Jan 2024)

Explosive Nucleosynthesis in Core-collapse Type II Supernovae: Insights from New C, N, Si, and Al–Mg Isotopic Compositions of Presolar Grains

  • Nan Liu,
  • Conel M. O’D. Alexander,
  • Bradley S. Meyer,
  • Larry R. Nittler,
  • Jianhua Wang,
  • Rhonda M. Stroud

DOI
https://doi.org/10.3847/2041-8213/ad18c7
Journal volume & issue
Vol. 961, no. 1
p. L22

Abstract

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We report C, N, Si, and Al–Mg isotope data for 39 presolar X silicon carbide (SiC) and four silicon nitride grains—a group of presolar grains that condensed in the remnants of core-collapse Type II supernovae (CCSNe)—isolated from the Murchison meteorite. Energy dispersive X-ray data were used to determine the Mg and Al contents of the X SiC grains for comparison with the Mg/Al ratios determined by secondary ion mass spectroscopy (SIMS). Previous SIMS studies have used O-rich standards in the absence of alternatives. In this study, the correlated isotopic and elemental data of the X SiC grains enabled accurate determination of the initial ^26 Al/ ^27 Al ratios for the grains. Our new grain data suggest that (i) the literature data for X grains are affected to varying degrees by asteroidal/terrestrial contamination, and (ii) the Al/Mg ratios in SiC are a factor of 2 (with ±6% 1 σ uncertainties) lower than estimated based on the SIMS analyses that used O-rich standards. The lowered Al/Mg ratios result in proportionally higher inferred initial ^26 Al/ ^27 Al ratios for presolar SiC grains. In addition, the suppression of asteroidal/terrestrial contamination in this study leads to the observation of negative trends for ^12 C/ ^13 C– ^30 Si/ ^28 Si and ^26 Al/ ^27 Al– ^30 Si/ ^28 Si among our CCSN grains. We discuss these isotope trends in the light of explosive CCSN nucleosynthesis models, based on which we provide new insights into several nontraditional CCSN nucleosynthesis processes, including explosive H burning, the existence of a C/Si zone in the outer regions of CCSNe, and neutrino–nucleus reactions in deep CCSN regions.

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