MSFA-Net applies multi-scale convolutions and soft frequency attention to LAMOST spectra, achieving high-precision BHB identification and adding 3583 new candidates to the catalog.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A large sample of blue horizontal-branch stars reveals that the Milky Way halo anisotropy increases from the center, stays radially dominated after removing merger debris, and shows older stars on colder, less radial orbits in the inner regions.
citing papers explorer
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MSFA-Net: An Advanced Deep Learning Model for Identifying Blue Horizontal-Branch Stars from LAMOST DR12
MSFA-Net applies multi-scale convolutions and soft frequency attention to LAMOST spectra, achieving high-precision BHB identification and adding 3583 new candidates to the catalog.
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Characterizing the velocity anisotropy of the Milky Way's stellar halo
A large sample of blue horizontal-branch stars reveals that the Milky Way halo anisotropy increases from the center, stays radially dominated after removing merger debris, and shows older stars on colder, less radial orbits in the inner regions.