A Machine Learning approach for correcting radial velocities using physical observables
arXiv:2301.12872 · doi:10.1051/0004-6361/202245092
Abstract
Precision radial velocity (RV) measurements continue to be a key tool to detect and characterise extrasolar planets. While instrumental precision keeps improving, stellar activity remains a barrier to obtain reliable measurements below 1-2 m/s accuracy. Using simulations and real data, we investigate the capabilities of a Deep Neural Network approach to produce activity free Doppler measurements of stars. As case studies we use observations of two known stars (Eps Eridani and AUMicroscopii), both with clear signals of activity induced RV variability. Synthetic data using the starsim code are generated for the observables (inputs) and the resulting RV signal (labels), and used to train a Deep Neural Network algorithm. We identify an architecture consisting of convolutional and fully connected layers that is adequate to the task. The indices investigated are mean line-profile parameters (width, bisector, contrast) and multi-band photometry. We demonstrate that the RV-independent approach can drastically reduce spurious Doppler variability from known physical effects such as spots, rotation and convective blueshift. We identify the combinations of activity indices with most predictive power. When applied to real observations, we observe a good match of the correction with the observed variability, but we also find that the noise reduction is not as good as in the simulations, probably due to the lack of detail in the simulated physics. We demonstrate that a model-driven machine learning approach is sufficient to clean Doppler signals from activity induced variability for well known physical effects. There are dozens of known activity related observables whose inversion power remains unexplored indicating that the use of additional indicators, more complete models, and more observations with optimised sampling strategies can lead to significant improvements in our detrending capabilities.
References in corpus (19)
- The Transiting Exoplanet Survey Satellite
- The generalised Lomb-Scargle periodogram. A new formalism for the floating-mean and Keplerian periodograms
- Spectrum radial velocity analyser (SERVAL). High-precision radial velocities and two alternative spectral indicators
- Trumpeting M Dwarfs with CONCH-SHELL: a Catalog of Nearby Cool Host-Stars for Habitable ExopLanets and Life
- SOAP 2.0: A tool to estimate the photometric and radial velocity variations induced by stellar spots and plages
- A planet within the debris disk around the pre-main-sequence star AU Microscopii
- Three years of Sun-as-a-star radial-velocity observations on the approach to solar minimum
- Stellar chromospheric activity of 1,674 FGK stars from the AMBRE-HARPS sample I. A catalogue of homogeneous chromospheric activity
- The CARMENES search for exoplanets around M dwarfs. Guaranteed time observations Data Release 1 (2016-2020)
- Investigating the young AU~Mic system with SPIRou: large-scale stellar magnetic field and close-in planet mass
- Identifying Exoplanets with Deep Learning II: Two New Super-Earths Uncovered by a Neural Network in K2 Data
- The HADES RV Programme with HARPS-N@TNG II. Data treatment and simulations
- High-contrast Imaging with Spitzer: Deep Observations of Vega, Fomalhaut, and epsilon Eridani
- One year of AU Mic with HARPS: I -- measuring the masses of the two transiting planets
- From Starspots to Stellar Coronal Mass Ejections -- Revisiting Empirical Stellar Relations
- Convective blueshift strengths of 810 F to M solar-type stars
- Constraining the Orbit and Mass of epsilon Eridani b with Radial Velocities, Hipparcos IAD-Gaia DR2 Astrometry, and Multi-epoch Vortex Coronagraphy Upper Limits
- Auto-correlation functions of astrophysical processes, and their relation to Gaussian processes; Application to radial velocities of different starspot configurations
- The differential rotation of epsilon Eri from MOST data