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.
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Pith papers citing it
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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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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.