YARARA V2: reaching sub-m s−1 precision over a decade using PCA on line-by-line radial velocities
Abstract:
Context. The detection of Earth-like planets with the radial velocity (RV) method is extremely challenging today due to the presence of non-Doppler signatures such as stellar activity and instrumental signals that mimic and hide the signals of exoplanets. In a previous paper, we presented the YARARA pipeline, which implements corrections for telluric absorption, stellar activity, and instrumental systematics at the spectral level, and then it extracts line-by-line (LBL) RVs with a significantly better precision than standard pipelines.
Aims. In this paper, we demonstrate that further gains in RV precision can be achieved by performing principal component analysis (PCA) decomposition on the LBL RVs.
Methods. The mean-insensitive nature of PCA means that it is unaffected by true Doppler shifts, and thus can be used to isolate and correct nuisance signals other than planets.
Results. We analysed the data of 20 intensively observed HARPS targets by applying our PCA approach on the LBL RVs obtained by YARARA. The first principal components show similarities across most of the stars and correspond to newly identified instrumental systematics for which we can now correct. For several targets, this results in an unprecedented RV root-mean-square of around 90 cm s−1 over the full lifetime of HARPS. We used the corrected RVs to confirm a previously published 120-day signal around 61 Vir, and to detect a super-Earth candidate (K ~ 60 ± 6 cm s−1, m sin i = 6.6 ± 0.7 M⊕) around the G6V star HD 20794, which spends part of its 600-day orbit within the habitable zone of the host star.
Conclusions. This study highlights the potential of LBL PCA to identify and correct hitherto unknown, long-term instrumental effects and thereby extend the sensitivity of existing and future instruments towards the Earth analogue regime.
YARARA V2: Reaching sub m/s precision over a decade using PCA on line-by-line RVs
Transit Timing Variations in the three-planet system: TOI-270
A simple method to estimate radial velocity variations due to stellar activity using photometry (vol 419, pg 3147, 2012)
Gaussian Process Regression for Astronomical Time Series
Abstract:
The past two decades have seen a major expansion in the availability, size, and precision of time-domain data sets in astronomy. Owing to their unique combination of flexibility, mathematical simplicity, and comparative robustness, Gaussian processes (GPs) have emerged recently as the solution of choice to model stochastic signals in such data sets. In this review, we provide a brief introduction to the emergence of GPs in astronomy, present the underlying mathematical theory, and give practical advice considering the key modeling choices involved in GP regression. We then review applications of GPs to time-domain data sets in the astrophysical literature so far, from exoplanets to active galactic nuclei, showcasing the power and flexibility of the method. We provide worked examples using simulated data, with links to the source code; discuss the problem of computational cost and scalability; and give a snapshot of the current ecosystem of open source GP software packages. In summary:▪ GP regression is a conceptually simple but statistically principled and powerful tool for the analysis of astronomical time series.
▪ It is already widely used in some subfields, such as exoplanets, and gaining traction in many others, such as optical transients.
▪ Driven by further algorithmic and conceptual advances, we expect that GPs will continue to be an important tool for robust and interpretable time domain astronomy for many years to come.