论文标题

四个反半乳性的旧式开放簇的光度法:Czernik 30,Berkeley 34,Berkeley 75和Berkeley 76

Photometry of the Four Anti-Galactocentric Old Open Clusters: Czernik 30, Berkeley 34, Berkeley 75, and Berkeley 76

论文作者

Im, Hyobin, Kim, Sang Chul, Kyeong, Jaemann, Park, Hong Soo, Lee, Joon Hyeop

论文摘要

我们使用Smarts 1.0 M望远镜在CTIO的Smarts 1.0 M望远镜获得的观察数据,对银河系中的四个旧开放簇(OC)进行了BVI光度研究。这四个OC位于抗半乳突方向和银河平面。我们使用红色团块和PARSEC等速线拟合方法确定了四个OC的基本物理参数,例如年龄,金属性,距离模量和颜色过量,并找到了四个OC的中心和大小。这四个旧的OC是2-3 Gyr Old,距离太阳距离为6-8 kpc。四个OC的金属度([Fe/H])值在-0.6和0.0 dex之间。我们将这四个OC的数据与来自五个文献的旧OC的数据相结合,导致236个对象研究银河径向金属分布。该半乳化[Fe/H]分布的单线性拟合的梯度为-0.052 +/- 0.004 dex/kpc。如果我们假设在这种径向金属分布中存在不连续性,则半乳半径<12 kpc处的梯度为-0.070 +/- 0.006 dex/kpc,而外部的梯度为-0.016 +/- 0.010,比内部部分的梯度为-016 +/- 0.010。尽管外部的样品簇不多,但断裂的线性拟合似乎更好地遵循观察数据。

We present a BVI photometric study of four old open clusters (OCs) in the Milky Way Galaxy, Czernik 30, Berkeley 34, Berkeley 75, and Berkeley 76 using the observation data obtained with the SMARTS 1.0 m telescope at the CTIO, Chile. These four OCs are located at the anti-Galactocentric direction and in the Galactic plane. We determine the fundamental physical parameters for the four OCs, such as age, metallicity, distance modulus, and color excess, using red clump and PARSEC isochrone fitting methods after finding center and size of the four OCs. These four old OCs are 2-3 Gyr old and 6 - 8 kpc away from the Sun. The metallicity ([Fe/H]) values of the four OCs are between -0.6 and 0.0 dex. We combine data for these four OCs with those for old OCs from five literatures resulting in 236 objects to investigate Galactic radial metallicity distribution. The gradient of a single linear fit for this Galactocentric [Fe/H] distribution is -0.052 +/- 0.004 dex/kpc. If we assume the existence of a discontinuity in this radial metallicity distribution, the gradient at Galactocentric radius < 12 kpc is -0.070 +/- 0.006 dex/kpc, while that at the outer part is -0.016 +/- 0.010 which is flatter than that of the inner part. Although there are not many sample clusters at the outer part, the broken linear fit seems to better follow the observation data.

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