Deep-learning based measurement of planetary radial velocities in the presence of stellar variability

Autor:	Colwell, Ian, Timmaraju, Virisha, Wise, Alexander
Rok vydání:	2023
Předmět:	Astrophysics - Earth and Planetary Astrophysics Astrophysics - Instrumentation and Methods for Astrophysics Astrophysics - Solar and Stellar Astrophysics Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Draft, unsubmitted, 10 pages, 8 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2304.04807 Zobrazit plný text záznamu View this record from Arxiv