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.
arXiv:2406.05447
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
ASTRAFier is a Transformer-BiLSTM-CNN model that classifies stellar variability from light curves, reporting 94.26% accuracy on Kepler data and 88.22% on TESS, then applied to 2.8 million TESS curves to release a catalog.
Simulations show that observed rotation in 13.5-Gyr-old alpha-rich stars constrains the Gaia-Sausage-Enceladus merger to mass ratios below 1:4, with interaction and starburst times both near 11 Gyr.
citing papers explorer
-
Inferring Asteroseismic Parameters from Short Observations Using Deep Learning: Application to TESS and K2 Red Giants
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.
-
ASTRAFier: A Novel and Scalable Transformer-based Stellar Variability Classifier
ASTRAFier is a Transformer-BiLSTM-CNN model that classifies stellar variability from light curves, reporting 94.26% accuracy on Kepler data and 88.22% on TESS, then applied to 2.8 million TESS curves to release a catalog.
-
Build-up and survival of the disc: From numerical models of galaxy formation to the Milky Way
Simulations show that observed rotation in 13.5-Gyr-old alpha-rich stars constrains the Gaia-Sausage-Enceladus merger to mass ratios below 1:4, with interaction and starburst times both near 11 Gyr.