SpecFANN: Spectral Fitting via Artificial Neural Networks I. A deep learning based fastwind emulator and fitting suite

arXiv:2607.17348 · astro-ph.SR, astro-ph.IM · Submitted 2026-07-19 · Read on arXiv

M. Abdul-Masih, C. Hawcroft, T. Lechien, S. Simon-Diaz, H. Sana, K. Deshmukh, J. Vrancken, J. I. Villaseñor, J. Müller-Horn, A. J. Kalita, B. Ludwig, J. Bodensteiner, D. M. Bowman, A. Escorza, G. Holgado, J. Puls, A. de Vicente

astro-ph.SR, astro-ph.IM

Submitted: 2026-07-19

Comments: Accepted for publication in A&A; 12 pages (+2 appendix pages), 9 figures (+2 appendix figures)

Code: https://github.com/MichaelAbdul-Masih/SpecFANN

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Abundance — 18× in this paper · explained in 1 episode
• Accuracy — 4× in this paper · explained in 1 episode
• Loss Function — 4× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• Activation Function — 2× in this paper · explained in 1 episode
• Calibration — 2× in this paper · explained in 7 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Metallicity — 2× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Markov Chain Monte Carlo — 1× in this paper
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• Deep Learning using Rectified Linear Units (ReLU)
• The Science Case for Multi-Object Spectroscopy on the European ELT
• The Nineteenth Data Release of the Sloan Digital Sky Survey

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