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
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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astro-ph.SR 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
SED analysis of blue stragglers in open clusters finds UV excesses indicating hot degenerate companions in 15 of 35 candidates, supporting binary evolution.
Multiwavelength study identifies 24 BSS candidates in Berkeley 18, derives their properties via SEDs, and infers binary evolution as the dominant channel from low dynamical interaction indicators.
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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Hot Degenerate Components in Blue Stragglers: A Multi-Wavelength SED Analysis of Nine Open Clusters with Swift/UVOT
SED analysis of blue stragglers in open clusters finds UV excesses indicating hot degenerate companions in 15 of 35 candidates, supporting binary evolution.
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Blue Straggler Stars in Berkeley 18: A Multiwavelength Study of Their Physical Properties and Dynamical Evolution
Multiwavelength study identifies 24 BSS candidates in Berkeley 18, derives their properties via SEDs, and infers binary evolution as the dominant channel from low dynamical interaction indicators.