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.
Gaia Data Release 2 Catalogue of Extremely-low Mass White Dwarf Candidates
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Extremely-low mass white dwarf stars (ELMs) are M < 0.3 MSun helium-core white dwarfs born either as a result of a common-envelope phase or after a stable Roche-lobe overflow episode in a multiple system. The Universe is not old enough for ELMs to have formed through single-star evolution channels. As remnants of binary evolution, ELMs can shed light onto the poorly understood phase of common-envelope evolution and provide constraints to the physics of mass accretion. Most known ELMs will merge in less than a Hubble time, providing an important contribution to the signal to be detected by upcoming space-based gravitational wave detectors. There are currently less than 150 known ELMs; most were selected by colour, focusing on hot objects, in a magnitude-limited survey of the Northern hemisphere only. Recent theoretical models have predicted a much larger spacial density for ELMs than estimated observationally based on this limited sample. In order to perform meaningful comparisons with theoretical models and test their predictions, a larger well-defined sample is required. In this work, we present a catalogue of ELM candidates selected from the second data release of Gaia (DR2). We have used predictions from theoretical models and analysed the properties of the known sample to map the space spanned by ELMs in the Gaia Hertzsprung-Russell diagram. Defining a set of colour cuts and quality flags, we have obtained a final sample of 5762 ELM candidates down to Teff ~ 5000K.
fields
astro-ph.SR 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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.