SINet outperforms five prior statistical and deep learning methods on F10.7 predictions and provides the first deep learning forecasts for the F30 solar index.
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Customized chromatic noise models for 67 pulsars detect non-dispersive delays in 21 cases, alter achromatic noise inferences in 19, and enable solar wind density estimates over 1.5 cycles.
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Daily Predictions of F10.7 and F30 Solar Indices with Deep Learning
SINet outperforms five prior statistical and deep learning methods on F10.7 predictions and provides the first deep learning forecasts for the F30 solar index.
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The NANOGrav 15 yr Data Set: Customized Chromatic Noise Models
Customized chromatic noise models for 67 pulsars detect non-dispersive delays in 21 cases, alter achromatic noise inferences in 19, and enable solar wind density estimates over 1.5 cycles.