3 citations · 3 across the 2 of their papers we have counts for
4 papers
Decoding Starlight with Big Survey Data, Machine Learning, and Cosmological Simulations
Kirsten Blancato
Stars, and collections of stars, encode rich signatures of stellar physics and galaxy evolution. With properties influenced by both their environment and intrinsic nature, stars re…
Data-driven derivation of stellar properties from photometric time series data using convolutional neural networks
Kirsten Blancato, Melissa Ness, Daniel Huber +2
Stellar variability is driven by a multitude of internal physical processes that depend on fundamental stellar properties. These properties are our bridge to reconciling stellar ob…
In the Galactic disk, stellar [Fe/H] and age predict orbits and precise [X/Fe]
Melissa K. Ness, Kathryn V. Johnston, Kirsten Blancato +4
We explore the structure of the element abundance--age--orbit distribution of the stars in the Milky Way's low- disk, by (re-)deriving precise [Fe/H], [X/Fe] and ages, along wit…
Variations in -element ratios trace the chemical evolution of the disk
Kirsten Blancato, Melissa Ness, Kathryn V. Johnston +2
It is well established that the chemical structure of the Milky Way exhibits a bimodality with respect to the -enhancement of stars at a given [Fe/H]. This has been studied larg…