AESTRA: Deep Learning for Precise Radial Velocity Estimation in the Presence of Stellar Activity

TL;DR

AESTRA is a deep learning approach that accurately estimates stellar radial velocities despite stellar activity interference, enabling detection of Earth-like planets with high precision.

Contribution

It introduces a self-supervised deep learning method combining spectrum auto-encoding and radial velocity estimation, effective without ground truth velocities.

Findings

Detects planetary signals as low as 0.1 m/s

Handles 3 m/s activity-induced noise

Operates without ground truth radial velocities

Abstract

Stellar activity interferes with precise radial velocity measurements and limits our ability to detect and characterize planets, particularly Earth-like planets. We introduce \aestra (Auto-Encoding STellar Radial-velocity and Activity), a deep learning method for precise radial velocity measurements. It combines a spectrum auto-encoder, which learns to create realistic models of the star's rest-frame spectrum, and a radial-velocity estimator, which learns to identify true Doppler shifts in the presence of spurious shifts due to line-profile variations. Being self-supervised, \aestra does not need "ground truth" radial velocities for training, making it applicable to exoplanet host stars for which the truth is unknown. In tests involving 1,000 simulated spectra, \aestra can detect planetary signals as low as 0.1 m/s even in the presence of 3 m/s of activity-induced noise and 0.3 m/s of photon noise per spectrum.