A data-driven decomposition of stellar abundance vectors into four latent patterns identifies distinct contributions from core-collapse supernovae, Type Ia supernovae, and AGB stars across the Milky Way disc.
Title resolution pending
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
years
2026 6representative citing papers
Reprojects abundances of 199k stars into 4 patterns, identifying enrichment pathways with strong chemo-spatial, age, and vertical correlations plus a transition at ~6 Gyr.
Milky Way abundance trends act as effective empirical proxies for nucleosynthetic yields, recovering alpha and Fe-peak abundances in quiescent galaxies with 0.05 dex median offset versus 0.23 dex for theory, indicating largely universal yields.
A uniform spectroscopic catalog of 625 exoplanet hosts shows subsolar-metallicity giant-planet hosts are alpha-enhanced relative to both iron-rich hosts and typical metal-poor field stars.
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.
PISP projects high-dimensional spectra into optimized subspaces using PCA or active subspaces plus L1 selection to raise accuracy and speed of stellar parameter inference over standard methods.
citing papers explorer
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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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PISP: Projected-Space Inference of Stellar Parameters
PISP projects high-dimensional spectra into optimized subspaces using PCA or active subspaces plus L1 selection to raise accuracy and speed of stellar parameter inference over standard methods.