Abstract
ABSTRACT Stellar magnetic activity produces time-varying distortions in the photospheric line profiles of solar-type stars. These lead to systematic errors in high-precision radial-velocity measurements, which limit efforts to discover and measure the masses of low-mass exoplanets with orbital periods of more than a few tens of days. We present a new data-driven method for separating Doppler shifts of dynamical origin from apparent velocity variations arising from variability-induced changes in the stellar spectrum. We show that the autocorrelation function (ACF) of the cross-correlation function used to measure radial velocities is effectively invariant to translation. By projecting the radial velocities on to a subspace labelled by the observation identifiers and spanned by the amplitude coefficients of the ACF’s principal components, we can isolate and subtract velocity perturbations caused by stellar magnetic activity. We test the method on a 5-yr time sequence of 853 daily 15-min observations of the solar spectrum from the HARPS-N instrument and solar-telescope feed on the 3.58-m Telescopio Nazionale Galileo. After removal of the activity signals, the heliocentric solar velocity residuals are found to be Gaussian and nearly uncorrelated. We inject synthetic low-mass planet signals with amplitude K = 40 cm s−1 into the solar observations at a wide range of orbital periods. Projection into the orthogonal complement of the ACF subspace isolates these signals effectively from solar activity signals. Their semi-amplitudes are recovered with a precision of ∼ 6.6 cm s−1, opening the door to Doppler detection and characterization of terrestrial-mass planets around well-observed, bright main-sequence stars across a wide range of orbital periods.
Highlights
Doppler spectroscopy has been one of the most productive methods to discover and characterize exoplanets
One feature of the method we present is that it makes use of existing data products, i.e. the cross-correlation functions (CCFs) between the observed spectra and a digital mask, and the radial velocities derived from them
We have presented a new algorithm for extracting precise radial velocity estimates from high-resolution spectroscopic planet surveys
Summary
Doppler spectroscopy has been one of the most productive methods to discover and characterize exoplanets. Improvements in the precision, wavelength calibration and stability of highresolution échelle spectrographs has allowed exoplanet surveys to probe planets with radial velocity (RV) semi-amplitudes of just ∼ 1 m s−1. The ability of spectroscopic surveys to detect and characterize low-mass planets is often limited by stellar variability and the stability of the wavelength calibration, rather than photon noise or instrumental errors (e.g., Saar & Donahue 1997; Queloz et al 2001; Haywood et al 2014). The purpose of the present study is to devise a practical new approach to measuring stellar radial velocities in a way that mitigates the errors due to line-shape changes caused by stellar variability. Related approaches exploiting profile-shape changes of even and odd character to disentangle shifts from activity have been published recently by Zhao & Tinney (2020) and Holzer et al (2020), while de Beurs et al (2020) have employed a neural-network machine-learning to relating activityrelated radial-velocity shifts to CCF profile-shape changes in the same solar dataset examined here
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call