A time-aware convolutional attention network trained on StarSim synthetic spectra reduces stellar activity radial velocity jitter to 52.5% and 62.4% of original levels in HARPS and CARMENES data for epsilon Eridani and TZ Arietis.
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The paper reviews ML applications for sequence modeling, pattern recognition, and generative Bayesian analysis to tackle heterogeneous data challenges in (exo)planetary science.
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Mitigating stellar radial velocity jitter using orthogonal activity indices and a time-aware neural network
A time-aware convolutional attention network trained on StarSim synthetic spectra reduces stellar activity radial velocity jitter to 52.5% and 62.4% of original levels in HARPS and CARMENES data for epsilon Eridani and TZ Arietis.
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Machine Learning as a Transformative Tool for (Exo-)Planetary Science
The paper reviews ML applications for sequence modeling, pattern recognition, and generative Bayesian analysis to tackle heterogeneous data challenges in (exo)planetary science.