Carbon star identification in Gaia DR3

We utilized the SHAP interpretability model to determine SHAP values for each feature in a spectrum, enabling us to explain the output of the "GaiaNet" model and provide further meaningful analysis. Compared to four traditional machine-learning methods, the "GaiaNet" model exhibits an average classification accuracy improvement of approximately 0.3% on the validation set, with the highest accuracy reaching 100%. Utilizing the SHAP model, we present a clear spectroscopic heatmap highlighting molecular band absorption features primarily distributed around CN773.3 and CN895.0, and summarize five key feature regions for carbon star identification.

Upon applying the trained classification model to the CSTAR sample with Gaia "xp_sampled_mean" spectra, we obtained 451 new candidate carbon stars as a by-product. Our algorithm is capable of discerning subtle feature differences from low-resolution spectra of Gaia, thereby assisting us in effectively identifying carbon stars with typically higher temperatures and weaker CN features, while providing compelling attributive explanations.

The interpretability analysis of deep learning holds significant potential in spectral identification.