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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6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
years
2026 6verdicts
UNVERDICTED 6representative citing papers
Derives deterministic MMD, KSD, and KL objectives with rotationally invariant kernels on the hypersphere, yielding more stable SSL training and dataset-dependent geometry in learned representations.
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.
Ages inferred for red giant stars via machine learning are generally insensitive to hyperparameters and architecture but somewhat sensitive to training set choice, especially for the oldest, coolest, and lowest-metallicity stars.
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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Expanding SPHERE-JEPA: A Family of Statistical Regularizers for the Hypersphere
Derives deterministic MMD, KSD, and KL objectives with rotationally invariant kernels on the hypersphere, yielding more stable SSL training and dataset-dependent geometry in learned representations.
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Stellar Age Compression Reshapes Interpretations of the Milky Way Thick-Disk Formation History
Stellar age compression in spectroscopic estimates can produce apparent rapid thick-disk formation signatures 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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Evaluating the Sensitivity of the Age Inferences of Red Giant Stars to Machine Learning Methodology
Ages inferred for red giant stars via machine learning are generally insensitive to hyperparameters and architecture but somewhat sensitive to training set choice, especially for the oldest, coolest, and lowest-metallicity stars.
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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.