AI 中文总结
本研究通过SPA项目获取的光谱，利用TSFitPy估算30个疏散星团81颗恒星的Mo、Ru、Zr丰度，发现其与s过程元素相关、与Eu偏差，为铁峰外重元素核合成提供了观测约束。
AI 中文摘要
背景：疏散星团是研究银河系化学演化的关键实验室，尤其是利用这些同星龄恒星群研究铁峰之外的元素，可为起源仍不确定的重元素核合成提供见解。目标：本研究旨在确定30个疏散星团中81颗恒星的钼（Mo）、钌（Ru）与锆（Zr）丰度，将这些新化学模式与本团队此前推导的关键参考元素（钇Y、锶Sr、铕Eu）的模式进行比较，以厘清不同核合成过程的独特贡献。方法：利用恒星族群天体物理学（Stellar Population Astrophysics, SPA）项目中，通过意大利国家望远镜（Telescopio Nazionale Galileo）上的HARPS-N阶梯光栅摄谱仪获取的高分辨率光谱，在局域热力学平衡（LTE）条件下，使用TSFitPy通过光谱合成估算化学丰度。结果：本研究提供了大样本疏散星团的首批钌丰度，以及钼与锆的新测量值；观测到钼、钌与其他s过程元素（锶、钇、锆）间存在紧密相关，斜率接近1，表明具有共同的增丰时标；相反，与r过程元素铕的关系存在显著偏差，意味着存在额外的核合成贡献；关键的是，涉及[Mo/Ru]和[s/Mo]比值的化学平面，与理论s过程预测相比存在系统偏移。
英文摘要
Context: Open clusters serve as key laboratories for studying the chemical evolution of the Milky Way. In particular, leveraging these coeval groups of stars to study elements beyond the iron peak provides insights into heavy-element nucleosynthesis, whose origins remain uncertain. Aims: This work aims to determine the Mo, Ru, and Zr abundances for 81 stars across 30 open clusters. We compare these new chemical patterns with those of key reference elements (Y, Sr, Eu), which were previously derived by our group. This comparison seeks to disentangle the distinct contributions from various nucleosynthetic processes. Methods: Using the high-resolution spectra acquired within the Stellar Population Astrophysics programme using the HARPS-N echelle spectrograph at the Telescopio Nazionale Galileo, we estimate chemical abundances through spectral synthesis with TSFitPy under local thermodynamic equilibrium (LTE) conditions. Results: This study provides the first Ru abundances for a large open cluster sample, alongside new measurements for Mo and Zr. We observe tight correlations between Mo, Ru, and other s-process elements (Sr, Y, Zr), with slopes near unity, suggesting common enrichment timescales. Conversely, relations with the r-process element Eu show significant deviations, implying additional nucleosynthetic contributions. Crucially, chemical planes involving [Mo/Ru] and [s/Mo] ratios suggest systematic offsets when compared to theoretical s-process predictions.
