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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UNVERDICTED 4representative citing papers
Stellar age compression in spectroscopic estimates can generate the appearance of rapid thick-disk formation in the Milky Way 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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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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Stellar Age Compression Reshapes Interpretations of the Milky Way Thick-Disk Formation History
Stellar age compression in spectroscopic estimates can generate the appearance of rapid thick-disk formation in the Milky Way without requiring an intrinsically bursty history.
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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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Self-consistent dynamical modelling of the Milky Way bar with orbital frequency analysis
An updated orbital frequency method shows multiple Milky Way bar lengths and pattern speeds are consistent with star data within 5 percent.