A systematic comparison of green valley selection criteria across multiparameter spaces using a homogeneous ultraviolet-optical dataset

Abstract

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.