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

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 Dn (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 Dn (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.

Improvements to AI Systems:

1. Diagnostic-Aware Classification Model

• Train a multi-label classifier that predicts a galaxy’s membership in multiple green valley (GV) definitions simultaneously (u−r, NUV−r, sSFR, Dn(4000)).

• The AI can output a “GV consistency score” indicating how robustly a galaxy is classified as transitional across diagnostics, flagging galaxies that are GV by only one criterion as ambiguous.

2. Causal Inference for Star Formation Quenching

• Use the paper’s finding that GV selection differences arise primarily from star formation activity, not stellar mass, to build a causal model (e.g., variational autoencoder or structural equation model) that separates mass-driven and star-formation-driven components.

• The AI can then predict the physical driver of a galaxy’s GV status (e.g., mass quenching vs. environmental quenching) rather than just assigning a binary label.

3. Domain-Adaptive Survey Merger

• Develop a transfer-learning system that maps GALEX+SDSS optical-UV features to a unified latent space, allowing the AI to apply GV criteria consistently across surveys with different wavelength coverage.

• The improved system can automatically recalibrate thresholds for new datasets, reducing diagnostic bias in large-scale sky surveys.

4. Uncertainty-Aware Galaxy Population Simulator

• Build a generative model (e.g., diffusion or normalizing flow) trained on the paper’s parameter-space distributions to synthesize mock GV galaxies.

• The AI can generate realistic samples conditioned on a chosen diagnostic, enabling tests of selection effects and helping design future observational campaigns.

5. Multi-Diagnostic Recommender for Follow-Up Observations

• Create an AI that, given a galaxy’s photometry and spectra, recommends the optimal combination of GV diagnostics (e.g., “use sSFR + NUV−r for low-mass galaxies, Dn(4000) for high-mass”) based on the paper’s overlap analysis.

• This reduces wasted telescope time by prioritizing galaxies with high cross-diagnostic agreement for detailed study.

What the Improved AI System Can Do:

• Provide a physically interpretable classification of transitional galaxies, distinguishing between robust and fragile GV members.

• Automatically adapt to new surveys without retraining, while quantifying the uncertainty introduced by diagnostic choice.

• Simulate realistic galaxy populations to test hypotheses about quenching mechanisms, and guide targeted observations for the most scientifically valuable GV candidates.