[2603.23899] SM-Net: Learning a Continuous Spectral Manifold from Multiple Stellar Libraries

Astrophysics > Instrumentation and Methods for Astrophysics arXiv:2603.23899 (astro-ph) [Submitted on 25 Mar 2026 (v1), last revised 27 Mar 2026 (this version, v2)] Title:SM-Net: Learning a Continuous Spectral Manifold from Multiple Stellar Libraries Authors:Omar Anwar, Aaron S. G. Robotham, Luca Cortese, Kevin Vinsen View a PDF of the paper titled SM-Net: Learning a Continuous Spectral Manifold from Multiple Stellar Libraries, by Omar Anwar and 3 other authors View PDF HTML (experimental) Abstract:We present SM-Net, a machine-learning model that learns a continuous spectral manifold from multiple high-resolution stellar libraries. SM-Net generates stellar spectra directly from the fundamental stellar parameters effective temperature (Teff), surface gravity (log g), and metallicity (log Z). It is trained on a combined grid derived from the PHOENIX-Husser, C3K-Conroy, OB-PoWR, and TMAP-Werner libraries. By combining their parameter spaces, we construct a composite dataset that spans a broader and more continuous region of stellar parameter space than any individual library. The unified grid covers Teff = 2,000-190,000 K, log g = -1 to 9, and log Z = -4 to 1, with spectra spanning 3,000-100,000 Angstrom. Within this domain, SM-Net provides smooth interpolation across heterogeneous library boundaries. Outside the sampled region, it can produce numerically smooth exploratory predictions, although these extrapolations are not directly validated against reference models. Zero or...