Salvaging Defective Spectra: Deep Learning Restoration of Defective Stellar Spectra

Large-scale spectroscopic surveys (e.g., LAMOST and DESI) inevitably contain a substantial fraction of defective spectra due to various observational and instrumental limitations. These defects, typically manifesting in the spectra as missing or corrupted contiguous regions, compromise downstream an...

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Detalhes bibliográficos

Principais autores:	Jingjing Wu, Yuchen He, Bin Jiang, Yanxia Zhang
Formato:	Artigo
Idioma:	Inglês
Publicado em:	IOP Publishing 2026-01-01
Colecção:	The Astrophysical Journal Supplement Series
Assuntos:	Astronomy data analysis Stellar astronomy Neural networks Surveys
Acesso em linha:	https://doi.org/10.3847/1538-4365/ae7e81
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