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arXiv 2511.04745astro-ph.GA
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SFMS周围亚吉年变异性及其对散射的贡献

Sub-Gyr variability around the SFMS and its contribution to the scatter

Universidad Complutense de Madrid(马德里康普顿斯大学);Instituto de Física de Partículas y del Cosmos, Universidad Complutense de Madrid(马德里康普顿斯大学粒子与宇宙物理研究所);Universidad Nacional Autónoma de México(墨西哥国立自治大学);Instituto de Astronomía, Universidad Nacional Autónoma de México(墨西哥国立自治大学天文学研究所);Instituto de Astrofísica de Canarias(加那利群岛天体物理学研究所)

A. Camps-Fariña, M. Chamorro-Cazorla, S. F. Sánchez

AI总结:

研究通过分析银河系在SFMS中的变异性,发现短期变异性对散射有贡献,但并非全部,同时揭示了恒星形成速率的自调节机制。

摘要:

我们旨在测量过去一吉年中银河系围绕恒星形成主序列(SFMS)的演化情况,以恒星质量为函数来量化其散射中有多大比例是由于短期变异性导致的。我们利用完整的光谱拟合,对马纳加调查中8960个星系的恒星形成历史进行了推导,以追踪过去一吉年中星系在SFMS中的位置。变异性与星系的恒星质量以及它们在SFMS和质量-金属含量关系(MZR)中的当前位置均相关,后者对SFR的变异性有显著影响。虽然大多数波动与随机性一致,但存在非常弱但统计上显著的约135-150年时间尺度的偏好。这些结果支持了银河系内SFR的强自调节性,确立了恒星形成爆发和熄灭事件的特征强度和时间尺度。我们还发现短期变异性无法解释SFMS散射的全部。它似乎在短期变异性与长期(晕级)分化中以相似程度出现,并符合模型的预测。

英文摘要:

We aim to measure the evolution of individual galaxies around the Star Formation Main Sequence (SFMS) during the last Gyr as a function of their stellar mass to quantify how much of its scatter is due to short-term variability.We derived star formation histories using full spectral fitting for a sample of 8,960 galaxies from the MaNGA survey to track the position of the galaxies in the SFMS during the last Gyr.The variability correlates with both the stellar mass of the galaxies and their current position in both the SFMS and the mass-metallicity relation (MZR), with the position in the latter strongly affecting variability in SFR. While most of the fluctuations are compatible with stochasticity, there is a very weak but statistically significant preference for $\sim135-150$ Myr time-scales. These results support a strong self-regulation of SFR within galaxies, establishing characteristic intensities and time-scales for bursts of star formation and quenching episodes. We also find that short-term variability cannot account for the entirety of the scatter in the SFMS. It appears to originate to a similar degree in short-term variability and long-term (halo-level) differentiation and fits predictions from models.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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