Nested sampling applied to ARIMA models enables Bayesian order selection and parameter inference that recovers ground truth in simulations and fits stochastic variability in sunspot, Kepler, and TESS light curves.
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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.
citing papers explorer
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Nested Sampling for ARIMA Model Selection in Astronomical Time-Series Analysis
Nested sampling applied to ARIMA models enables Bayesian order selection and parameter inference that recovers ground truth in simulations and fits stochastic variability in sunspot, Kepler, and TESS light curves.
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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.