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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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
Stellar age compression in spectroscopic estimates can produce apparent rapid thick-disk formation signatures without requiring an intrinsically bursty history.
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.
An updated orbital frequency method shows multiple Milky Way bar lengths and pattern speeds are consistent with star data within 5 percent.
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.