A single end-to-end Transformer model unifies stellar labels from heterogeneous spectroscopic surveys into a self-consistent scale without post-hoc recalibration.
@doi [ ] 10.3847/1538-4357/aadba5, https://ui.adsabs.harvard.edu/#abs/2018ApJ...865...96F 865
3 Pith papers cite this work. Polarity classification is still indexing.
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FIRE-2 simulations show that stellar radial redistribution scatter saturates at ~2 kpc for stars older than ~3 Gyr, with net orbital changes depending on age and current radius, broadly matching Milky Way observations.
Interstellar objects may contribute enough baryonic mass to reduce the local dark matter halo density to 0.24 GeV/cm³.
citing papers explorer
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Homogeneous Stellar Parameters from Heterogeneous Spectra with Deep Learning
A single end-to-end Transformer model unifies stellar labels from heterogeneous spectroscopic surveys into a self-consistent scale without post-hoc recalibration.
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Radial redistribution of stellar orbits in FIRE simulations of Milky-Way-mass galaxies
FIRE-2 simulations show that stellar radial redistribution scatter saturates at ~2 kpc for stars older than ~3 Gyr, with net orbital changes depending on age and current radius, broadly matching Milky Way observations.
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Contribution of interstellar objects to local dark matter density
Interstellar objects may contribute enough baryonic mass to reduce the local dark matter halo density to 0.24 GeV/cm³.