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.
@doi [ ] 10.3847/1538-4357/ae0b64, https://ui.adsabs.harvard.edu/abs/2025ApJ...994...52M 994
2 Pith papers cite this work. Polarity classification is still indexing.
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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.
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Milky Way Mapper decoded abundances -- I. Shared disc enrichment patterns
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.
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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.