Deep learning infers Δν and ν_max from one-month TESS and K2 observations of red giants with reliable results for ~50% of Kepler/K2 samples and ~23% of TESS stars, plus ΔΠ1 for ~200 K2 young red giants that match known patterns.
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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.
C/N/O abundance patterns indicate that some α-rich young red giants are merger or mass-transfer products rather than genuinely young stars with anomalous chemistry.
citing papers explorer
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Inferring Asteroseismic Parameters from Short Observations Using Deep Learning: Application to TESS and K2 Red Giants
Deep learning infers Δν and ν_max from one-month TESS and K2 observations of red giants with reliable results for ~50% of Kepler/K2 samples and ~23% of TESS stars, plus ΔΠ1 for ~200 K2 young red giants that match known patterns.
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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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Origin of $\alpha$-rich young stars: clues from C, N and O
C/N/O abundance patterns indicate that some α-rich young red giants are merger or mass-transfer products rather than genuinely young stars with anomalous chemistry.