Deep Learning–driven Atmospheric Parameter Prediction for Hot Subdwarf Stars with Synthetic and Observed Spectra
We design a convolutional neural network incorporating channel attention and spatial attention mechanisms to predict atmospheric parameters of hot subdwarfs. The experimental dataset comprises spectra at nine distinct signal-to-noise ratio (SNR) levels, with each SNR level containing 11,396 syntheti...
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| Hlavní autoři: | , , , , , , |
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| Médium: | Artigo |
| Jazyk: | Inglês |
| Vydáno: |
IOP Publishing
2026-01-01
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| Edice: | The Astrophysical Journal Supplement Series |
| Témata: | |
| On-line přístup: | https://doi.org/10.3847/1538-4365/ae374f |
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