Hybrid deep learning models recover large frequency separation, frequency of maximum power, and dipole period spacing from low-resolution Gaia XP spectra with accuracy comparable to moderate-resolution spectroscopy.
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3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A new sample of young candidate Bridge stars is identified and shown to align with gas structures, with kinematics implying a ~125 Myr crossing time consistent with the last LMC-SMC interaction.
DSC classifiers achieve at least 88% completeness and 96% purity for bright extragalactic sources in Gaia DR4, with mid-IR data boosting faint-source completeness at modest purity cost.
citing papers explorer
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Potential of Gaia XP Spectra in Red Giant Star Asteroseismology: A Deep-Learning Approach
Hybrid deep learning models recover large frequency separation, frequency of maximum power, and dipole period spacing from low-resolution Gaia XP spectra with accuracy comparable to moderate-resolution spectroscopy.
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Characterisation of the Clouds' young stellar Bridge using Gaia DR3
A new sample of young candidate Bridge stars is identified and shown to align with gas structures, with kinematics implying a ~125 Myr crossing time consistent with the last LMC-SMC interaction.
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Performance analysis of extragalactic classifications in Gaia Data Release 4
DSC classifiers achieve at least 88% completeness and 96% purity for bright extragalactic sources in Gaia DR4, with mid-IR data boosting faint-source completeness at modest purity cost.