AI 中文总结
该研究利用GALEX与SDSS的同质紫外-光学数据集，系统比较不同绿谷选择判据的差异，发现其识别的星系子集不同，需结合互补诊断工具研究过渡星系。
AI 中文摘要
我们通过检验常用绿谷（GV）选择判据在多个观测与物理参数空间的分布，对这些判据开展系统比较。利用由银河演化探测器（GALEX）与斯隆数字巡天（SDSS）构建的同质紫外-光学数据集，我们基于静止帧u-r、NUV-r色指数、恒星形成率（sSFR）及Dn(4000)谱指数构建GV样本，在色-恒星质量、色-星等、恒星形成率-恒星质量图中对这些样本进行分析。我们发现，不同选择判据识别出的GV星系是统计上不同的子集，占据参数空间的不同区域：基于紫外的选择在NUV-r色空间中分布紧凑，但在恒星形成率-恒星质量平面中偏向光学红星系和较低恒星形成活动；u-r选择的样本在光学色空间中约束更紧密，但偏向较高恒星形成率；基于Dn(4000)的选择得到的样本异质性最强；相比之下，基于sSFR选择的GV样本在所有参数空间中表现最一致。尽管存在这些差异，所有选择方法覆盖的恒星质量范围相似，表明观测到的差异主要源于恒星形成活动的差异而非恒星质量。不同选择判据之间相对较小的重叠表明，GV的识别强烈依赖于诊断工具，常用的一维定义不可互换。这些结果强调，结合互补诊断工具对获取过渡星系群体更完整、具物理意义的图像至关重要。
英文摘要
We present a systematic comparison of commonly adopted green valley (GV) selection criteria by examining their distributions across multiple observational and physical parameter spaces. Using a homogeneous ultraviolet-optical dataset constructed from the Galaxy Evolution Explorer (GALEX) and the Sloan Digital Sky Survey (SDSS), we construct GV samples based on rest-frame $u-r$ and NUV$-r$ colours, specific star formation rate, and the $D_n(4000)$ spectral index. These samples are analysed in colour--stellar mass, colour--magnitude, and star formation rate--stellar mass diagrams. We find that the different selection criteria identify statistically distinct subsets of GV galaxies occupying different regions of parameter space. Ultraviolet-based selections are compact in NUV$-r$ colour space but shift toward optically red galaxies and lower star formation activity in the star formation rate--stellar mass plane. The $u-r$-selected sample is more tightly confined in optical colour space but is biased toward higher star formation rates, whereas the $D_n(4000)$-based selection yields the most heterogeneous population. In contrast, the sSFR-selected GV sample exhibits the most consistent behaviour across all parameter spaces. Despite these differences, all selection methods span a similar stellar mass range, indicating that the observed variations arise primarily from differences in star formation activity rather than stellar mass. The relatively small overlap between the different selection criteria demonstrates that GV identification is strongly diagnostic-dependent and that the commonly adopted one-dimensional definitions are not interchangeable. These results highlight the importance of combining complementary diagnostics to obtain a more complete and physically meaningful picture of transitional galaxy populations.
Comments12 pages, 6 figures
