AESTRA: Deep Learning for Precise Radial Velocity Estimation in the Presence of Stellar Activity
Abstract
Stellar activity interferes with precise radial velocity measurements and limits our ability to detect and characterize planets, particularly Earth-like planets. We introduce (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, 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, 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.
Turn this paper into a full lesson
ArcXiv compiles a staged curriculum from this paper: 8-12 lessons across beginner → advanced, synthesised section guides, visuals, flashcards, a quiz, exercises, and on-demand deep dives per section. Grounded in the abstract, never invented.