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.
J., Kjeldsen, H., Bedding, T
3 Pith papers cite this work. Polarity classification is still indexing.
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astro-ph.SR 3verdicts
UNVERDICTED 3representative citing papers
In a bias-cleaned sample of main-sequence stars, magnetic activity above solar maximum accounts for non-detection of p-modes in 32% of cases where amplitude is predicted sufficient, while stars with photometric activity index above 2000 ppm have 98.3% probability of no detected oscillations.
Magnetic activity induces frequency shifts that bias asteroseismic age estimates by up to 10% and helium abundance by up to 3% in solar-like stars.
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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Revisiting the impact of stellar magnetic activity on the detectability of solar-like oscillations by Kepler
In a bias-cleaned sample of main-sequence stars, magnetic activity above solar maximum accounts for non-detection of p-modes in 32% of cases where amplitude is predicted sufficient, while stars with photometric activity index above 2000 ppm have 98.3% probability of no detected oscillations.
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Influence of magnetic activity on the determination of stellar parameters through asteroseismology
Magnetic activity induces frequency shifts that bias asteroseismic age estimates by up to 10% and helium abundance by up to 3% in solar-like stars.