Machine Learning versus Spectral Energy Distribution Fitting: A Comparative Analysis of Accuracy in Stellar Mass Estimation

Traditional spectral energy distribution (SED)–fitting methods for stellar mass estimation face persistent challenges including systematic biases and computational constraints. We present a controlled comparison of machine learning (ML) and SED-fitting methods, assessing their accuracy, robustness, and computational efficiency. Using a sample of COSMOS-like galaxies from the Horizon-Active Galactic Nucleus (AGN) simulation as a benchmark with known true masses, we evaluate the parametric t-distributed stochastic neighbor embedding (Pt-SNE) algorithm trained on noise-injected G. Bruzual & S. Charlot models against the established SED-fitting code LePhare. Our results demonstrate that Pt-SNE achieves superior accuracy, with an rms error (σF) of 0.169 dex compared to LePhare’s 0.306 dex. Crucially, Pt-SNE exhibits significantly lower bias (0.029 dex) compared to LePhare (0.286 dex). Pt-SNE also shows greater robustness across all stellar mass ranges, particularly for low-mass galaxies (109–1010 M⊙), where it reduces errors by 47%–53%. Even when restricted to only six optical bands, Pt-SNE outperforms LePhare using all 26 available photometric bands, underscoring its superior informational efficiency. Computationally, Pt-SNE processes large data sets ∼3.2 × 103 times faster than LePhare. These findings highlight the fundamental advantages of ML methods for stellar mass estimation, demonstrating their potential to deliver more accurate, stable, and scalable measurements for large-scale galaxy surveys.