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.
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3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
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.
The HAges catalog compiles published asteroseismic and gyrochronological ages for 659 HWO target stars, finding that only ~5% have asteroseismic ages and ~20% have gyrochronal ages.
citing papers explorer
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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.
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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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The HAges Catalog: Stellar Ages for High Priority HWO Target Stars
The HAges catalog compiles published asteroseismic and gyrochronological ages for 659 HWO target stars, finding that only ~5% have asteroseismic ages and ~20% have gyrochronal ages.