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

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Stellar activity interferes with precise radial velocity measurements and limits our ability to detect and characterize planets, in particular 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 autoencoder, 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 1000 simulated spectra, AESTRA can detect planetary signals as low as 0.1 m s−1 even in the presence of 3 m s−1 of activity-induced noise and 0.3 m s−1 of photon noise per spectrum.

Original languageEnglish (US)
Article number23
JournalAstronomical Journal
Volume167
Issue number1
DOIs
StatePublished - Jan 1 2024

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science

Fingerprint

Dive into the research topics of 'AESTRA: Deep Learning for Precise Radial Velocity Estimation in the Presence of Stellar Activity'. Together they form a unique fingerprint.

Cite this