利用Swift/UVOT对Berkeley 39的蓝离散星和黄离散星的表征研究
Characterization of blue and yellow straggler stars of Berkeley 39 using Swift/UVOT
AI总结：
本研究利用多波段观测数据，通过机器学习算法识别Berkeley 39的星团成员，构建候选离散星的SED并拟合，分析其紫外超量等性质，表征该星团的蓝、黄离散星。
摘要：
我们利用包括Swift/UVOT在内的多波段观测，对疏散星团（OC）Berkeley 39的蓝离散星（BSS）和黄离散星（YSS）进行表征。我们的分析还使用了来自GALEX的紫外（UV）数据、来自Gaia DR3和Pan-STARRS的光学数据，以及来自2MASS、Spitzer/IRAC和WISE的红外数据。Berkeley 39是一个年龄约为60亿年的银河系疏散星团，距离约为4200秒差距。我们通过在Gaia DR3数据上使用机器学习算法ML-MOC，识别出729个源为星团成员。其中，17个源被归类为BSS候选体，4个被归类为YSS候选体。我们构建了Swift/UVOT视场内16个BSS候选体和2个YSS候选体的多波段光谱能量分布（SED），以分析它们的性质。在这些候选体中，8个BSS候选体和全部2个YSS候选体成功用单组分SED拟合。5个BSS候选体在近紫外波段表现出轻微超量（除一个UVOT滤光片外，其余所有滤光片的分数残差均<0.3），而3个BSS候选体在近紫外波段表现出中等至显著超量（至少两个UVOT滤光片的分数残差>0.3）。我们给出了基于SED拟合估算的BSS和YSS候选体的性质。
英文摘要：
We characterize blue straggler stars (BSS) and yellow straggler stars (YSS) of an open cluster (OC) Berkeley 39 using multi-wavelength observations including Swift/UVOT. Our analysis also makes use of ultraviolet (UV) data from GALEX, optical data from Gaia DR3 and Pan-STARRS, and infrared data from 2MASS, Spitzer/IRAC, and WISE. Berkeley 39 is a ~6 Gyr old Galactic OC located at a distance of ~4200 pc. We identify 729 sources as cluster members utilizing a machine learning algorithm, ML-MOC, on Gaia DR3 data. Of these, 17 sources are classified as BSS candidates and four as YSS candidates. We construct multi-wavelength spectral energy distributions (SEDs) of 16 BSS and 2 YSS candidates, within the Swift/UVOT field, to analyze their properties. Out of these, 8 BSS candidates and both the YSS candidates are successfully fitted with single-component SEDs. Five BSS candidates show marginal excess in the near-UV (fractional residual < 0.3 in all but one UVOT filter), whereas three BSS candidates show moderate to significant excess in the near-UV (fractional residual > 0.3 in at least two UVOT filters). We present the properties of the BSS and YSS candidates, estimated based on the SED fits.
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