论文标题

附近的分段的周期露出关系$δ$Δ$ scuti星星

A segmented period-luminosity relation for nearby extragalactic $δ$ Scuti stars

论文作者

Martínez-Vázquez, C. E., Salinas, R., Vivas, A. K., Catelan, M.

论文摘要

银河系的周期露光关系(PLR)$δ$ scuti($δ$ sct)星星已经通过线性关系描述了今天。但是,在研究诸如麦哲伦云和几个矮星系等诸如麦芽素云之类的外层系统时,我们首次注意到$δ$ SCT星的PLR中的非线性行为。使用$ \ sim 3700 $的最大样本从文献中可用的数据中获取的数据 - $ - $ - $ - $ - $基于大麦芽云(LMC)$ - $ - $ - 我们获得以下logition-prig-log的logitiation-prig-log / prigant prigant pramee 3 champ prig - \ pm 0.01 $(或$ 0.093 \ pm 0.002 $ d)的时间(SP)(SP)和更长的时间(LP)比突发点:$$ M_V^{sp} = -7.08(\ pm 0.25) -1.03$$ $$M_V^{lp} = M_V^{sp} + 4.38 (\pm 0.32) \cdot (\log{P} + 1.03 (\pm 0.01));\hspace{5pt} \log{P} \geq -1.03$$ Geometric or depth effects in the LMC, metallicity dependence,或将不同的脉动模式丢弃,作为在乳清外$δ$ sct星中看到的这种分段PLR的可能原因。根据当前数据,$ \ sim 0.09 $ days的分段关系的起源仍无法解释。

The period-luminosity relations (PLR) of Milky Way $δ$ Scuti ($δ$ Sct) stars have been described to the present day by a linear relation. However, when studying extragalactic systems such as the Magellanic Clouds and several dwarf galaxies, we notice for the first time a non-linear behaviour in the PLR of $δ$ Sct stars. Using the largest sample of $\sim 3700$ extragalactic $δ$ Sct stars from data available in the literature $-$mainly based on OGLE and SuperMACHO survey in the Large Magellanic Cloud (LMC)$-$ we obtain that the best fit to the period-luminosity ($M_V$) plane is given by the following piecewise linear relation with a break at $\log{P} = -1.03 \pm 0.01$ (or $0.093 \pm 0.002$ d) for shorter periods (sp) and longer periods (lp) than the break-point: $$M_V^{sp} = -7.08 (\pm 0.25) \log{P} -5.74 (\pm 0.29) ;\hspace{5pt} \log{P} < -1.03$$ $$M_V^{lp} = M_V^{sp} + 4.38 (\pm 0.32) \cdot (\log{P} + 1.03 (\pm 0.01));\hspace{5pt} \log{P} \geq -1.03$$ Geometric or depth effects in the LMC, metallicity dependence, or different pulsation modes are discarded as possible causes of this segmented PLR seen in extragalactic $δ$ Sct stars. The origin of the segmented relation at $\sim 0.09$ days remains unexplained based on the current data.

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