Revealing α -element’s Past with Subaru/IRD: Oxygen Abundance of 35 Very Metal-poor Stars from Near-IR OH Lines

Le résumé fourni par la source

Abstract Oxygen abundances in very and extremely metal-poor (V/EMP) stars provide critical constraints on early massive stars’ nucleosynthesis. An oxygen abundance analysis is presented for 35 V/EMP stars (−4.0 < [Fe/H] < −1.5) using near-infrared (NIR) H -band OH vibrorotational lines from high-resolution Subaru Infrared Doppler spectra. To examine the reliability of these NIR OH lines, the results are compared with the abundances obtained from the 3D non–local thermodynamic equilibrium (NLTE)-insensitive forbidden [O I ] 6300 Å line using archival high-resolution optical spectra. After homogeneously rederiving stellar parameters and 1D/NLTE Fe abundances using Gaia photo-astrometry and literature optical Fe equivalent width data, oxygen abundance from OH and [O I ] lines is determined through 1D local thermodynamic equilibrium (LTE) spectral synthesis. A sensitivity analysis confirms that NIR OH lines are highly sensitive to the adopted temperature ( Δ log ϵ ( O ) / Δ T ∼ ± 0.25 dex / ± 100 K ) compared to the forbidden line. A temperature-dependent discrepancy between the tracers is identified: in cool red giants ( T eff ≲ 4600 K), OH-based abundances are systematically lower than [O I ]-based abundances by 0.05–0.25 dex, while warmer red giants show higher OH-based abundances as expected from 3D effects. Despite this systematic offset, the numerous measurable NIR OH lines yield significantly smaller random abundance errors than that of the single, weak [O I ] line. Leveraging this statistical precision, an empirical calibration as a function of T eff , log g , [Fe/H], and [C/Fe] is derived to align the 1D/LTE OH abundances onto the [O I ] scale. Applying this correction substantially reduces the scatter and temperature dependence in the [O/Fe] versus [Fe/H] plane and flattens the trend, bringing the results into fairly good agreement with Galactic chemical evolution models.

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