Deep-learning based measurement of planetary radial velocities in the presence of stellar variability
This work addresses the challenge of detecting small exoplanets for astronomers by mitigating stellar RV jitter, though it appears incremental as it builds on existing deep-learning methods for spectral analysis.
The researchers tackled the problem of measuring small planetary radial velocities (RVs) in the presence of stellar variability by developing a deep-learning approach using neural networks on HARPS-N spectra, achieving recovery of planets with 0.2 m/s semi-amplitude and 50-day period with 8.8% error in amplitude and 0.7% in period.
We present a deep-learning based approach for measuring small planetary radial velocities in the presence of stellar variability. We use neural networks to reduce stellar RV jitter in three years of HARPS-N sun-as-a-star spectra. We develop and compare dimensionality-reduction and data splitting methods, as well as various neural network architectures including single line CNNs, an ensemble of single line CNNs, and a multi-line CNN. We inject planet-like RVs into the spectra and use the network to recover them. We find that the multi-line CNN is able to recover planets with 0.2 m/s semi-amplitude, 50 day period, with 8.8% error in the amplitude and 0.7% in the period. This approach shows promise for mitigating stellar RV variability and enabling the detection of small planetary RVs with unprecedented precision.