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Chemical abundance inventory in phosphorus-rich stars

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arxiv 2503.03569 v1 pith:UF5J4SYQ submitted 2025-03-05 astro-ph.SR astro-ph.GA

classification astro-ph.SRastro-ph.GA
keywords starsabundancechemicalp-richanalysisapogee-2detailedelements
verification ladder T0 review T1 audit T2 compute T3 formal
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We provide an overview of the latest advances in the study of phosphorus-rich stars, covering their detailed chemical abundance analyses and innovative mining approaches. Following the discovery of 16 low-mass and low-metallicity stars rich in P, we expanded this sample by demonstrating that a recently identified group of Si-rich giants is also P-rich. A detailed abundance analysis was conducted on the nearinfrared spectra from APOGEE-2 DR17, encompassing 13 elements. Subsequently, a similar analysis was performed on the optical UVES spectra of four P-rich stars, resulting in the abundance determination of 48 light and heavy elements. This comprehensive analysis further refined the chemical fingerprint of these peculiar stars, which was employed to evaluate the plausibility of various nucleosynthetic formation scenarios. In order to obtain a statistically more reliable chemical fingerprint in the future, we explored the use of unsupervised machine learning algorithms to identify additional P-rich stars in extensive spectroscopic surveys, such as APOGEE-2. The primary objective of this research is to identify the progenitor of these stars and determine whether current nucleosynthetic models require revision or if a completely new source of P in the Galaxy is responsible for the existence of the P-rich stars.

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  1. Classifying metal-poor stars with machine learning using nucleosynthesis calculations

    nucl-th 2025-05 conditional novelty 6.0 of 10

    Machine learning trained on simulated r- and s-process nucleosynthesis patterns classifies metal-poor stars in agreement with conventional labels 87% of the time and suggests some standard classifications are wrong.

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