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

Abstract 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 ( σ F ) 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 (10 9 ​​​​​​–10 10 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 × 10 3 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.