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Found 6 papers, 3 papers with code
Disentangled Representation Learning for Astronomical Chemical Tagging
To remove known non-chemical factors of variation, we develop and implement a neural network architecture that learns a disentangled spectral representation.
The COMBS Survey -- II. Distinguishing the Metal-Poor Bulge from the Halo Interlopers
Currently, it is unclear if the metal-poor stars in the bulge ([Fe/H] < -1 dex) are merely halo interlopers, a unique accreted population, part of the boxy/peanut-shaped (B/P) bulge or a classical bulge population.
Astrophysics of Galaxies
An unsupervised method for identifying $X$-enriched stars directly from spectra: Li in LAMOST
Stars with peculiar element abundances are important markers of chemical enrichment mechanisms.
Solar and Stellar Astrophysics
Data-driven derivation of stellar properties from photometric time series data using convolutional neural networks
Stellar variability is driven by a multitude of internal physical processes that depend on fundamental stellar properties.
Solar and Stellar Astrophysics
Actions are weak stellar age indicators in the Milky Way disk
We find weak gradients in all actions with stellar age, of (7. 51 +/- 0. 52, -29. 0 +/- 1. 83, 1. 54 +/- 0. 18) kpc km/s/Gyr for J_r, L_z, and J_z, respectively.
Solar and Stellar Astrophysics Astrophysics of Galaxies
The Cannon: A data-driven approach to stellar label determination
New spectroscopic surveys offer the promise of consistent stellar parameters and abundances ('stellar labels') for hundreds of thousands of stars in the Milky Way: this poses a formidable spectral modeling challenge.
Solar and Stellar Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
