TheUse of Conditional Variational Autoencoders in Generating Stellar Spectra
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912551411908608 |
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| author | Gebran, Marwan Bentley, Ian |
| author_facet | Gebran, Marwan Bentley, Ian |
| contents | We present a conditional variational autoencoder (CVAE) that generates stellar spectra covering 4000 $\le$ $T_{\mathrm{eff}$ $\le$ 11,000 K, $2.0 \le \log g \le 5.0$ dex, $-1.5 \le [\mathrm{M}/\mathrm{H}] \le +1.5$ dex, $v\sin i \le 300$ km/s, $ξ_t$ between 0 and 4 km/s, and for any instrumental resolving powers less than 115,000. The spectra can be calculated in the wavelength range 4450-5400 Å. Trained on a grid of \textsc{SYNSPEC} spectra, the network synthesizes a spectrum in around two orders of magnitude faster than line-by-line radiative transfer. We validate the CVAE on $10^4$ test spectra unseen during training. Pixel-wise statistics yield a median absolute residual of <$1.8\times10^{-3}$ flux units with no wavelength-dependent bias. A residual error map across the parameters plane shows $\langle|ΔF|\rangle<2\times10^{-3}$ everywhere, and marginal diagnostics versus $T_{\mathrm{eff}}$, $\log g$, $v\sin i$, $ξ_t$, and $[Fe/H]$\ reveal no relevant trends. These results demonstrate that the CVAE can serve as a drop-in, physics-aware surrogate for radiative transfer codes, enabling real-time forward modeling in stellar parameter inference and offering promising tools for spectra synthesis for large astrophysical data analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17059 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TheUse of Conditional Variational Autoencoders in Generating Stellar Spectra Gebran, Marwan Bentley, Ian Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics Computational Physics Space Physics We present a conditional variational autoencoder (CVAE) that generates stellar spectra covering 4000 $\le$ $T_{\mathrm{eff}$ $\le$ 11,000 K, $2.0 \le \log g \le 5.0$ dex, $-1.5 \le [\mathrm{M}/\mathrm{H}] \le +1.5$ dex, $v\sin i \le 300$ km/s, $ξ_t$ between 0 and 4 km/s, and for any instrumental resolving powers less than 115,000. The spectra can be calculated in the wavelength range 4450-5400 Å. Trained on a grid of \textsc{SYNSPEC} spectra, the network synthesizes a spectrum in around two orders of magnitude faster than line-by-line radiative transfer. We validate the CVAE on $10^4$ test spectra unseen during training. Pixel-wise statistics yield a median absolute residual of <$1.8\times10^{-3}$ flux units with no wavelength-dependent bias. A residual error map across the parameters plane shows $\langle|ΔF|\rangle<2\times10^{-3}$ everywhere, and marginal diagnostics versus $T_{\mathrm{eff}}$, $\log g$, $v\sin i$, $ξ_t$, and $[Fe/H]$\ reveal no relevant trends. These results demonstrate that the CVAE can serve as a drop-in, physics-aware surrogate for radiative transfer codes, enabling real-time forward modeling in stellar parameter inference and offering promising tools for spectra synthesis for large astrophysical data analysis. |
| title | TheUse of Conditional Variational Autoencoders in Generating Stellar Spectra |
| topic | Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics Computational Physics Space Physics |
| url | https://arxiv.org/abs/2508.17059 |