SM-Net: Learning a Continuous Spectral Manifold from Multiple Stellar Libraries
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 3,0...
| Authors | Omar Anwar, Aaron S. G. Robotham, Luca Cortese et al. |
|---|---|
| Primary category | astro-ph.IM |
| Published | 25 March 2026 |
| Last updated at source | 27 March 2026 |
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