Search

Radial-velocity (RV) signals induce RV variations an order of magnitude larger than the signal created by the orbit of Earth-twins, thus preventing their detection. The goal of this paper is to compare the efficiency of the different methods used to deal with stellar signals to recover extremely low-mass planets despite. However, because observed RV variations at the m/s precision level or below is a combination of signals induced by unresolved orbiting planets, by the star, and by the instrument, performing such a comparison using real data is extremely challenging. To circumvent this problem, we generated simulated RV measurements including realistic stellar and planetary signals. Different teams analyzed blindly those simulated RV measurements, using their own method to recover planetary signals despite stellar RV signals. By comparing the results obtained by the different teams with the planetary and stellar parameters used to generate the simulated RVs, it is therefore possible to compare the efficiency of these different methods. The most efficient methods to recover planetary signals {take into account the different activity indicators,} use red-noise models to account for stellar RV signals and a Bayesian framework to provide model comparison in a robust statistical approach. Using the most efficient methodology, planets can be found down to K/N= K_pl/RV_rms*sqrt{N_obs}=5 with a threshold of K/N=7.5 at the level of 80-90% recovery rate found for a number of methods. These recovery rates drop dramatically for K/N smaller than this threshold. In addition, for the best teams, no false positives with K/N > 7.5 were detected, while a non-negligible fraction of them appear for smaller K/N. A limit of K/N = 7.5 seems therefore a safe threshold to attest the veracity of planetary signals for RV measurements with similar properties to those of the different RV fitting challenge systems., 36 pages (including 10 pages of appendix), 23 figures, Accepted in A&A

We carry out a comparative analysis of the performance of three algorithms widely used to identify significant periodicities in radial-velocity (RV) datasets: the Generalised Lomb-Scargle Periodogram (GLS), its modified version based on Bayesian statistics (BGLS), and the multi-frequency periodogram scheme called FREquency DEComposer (FREDEC). We apply the algorithms to a suite of numerical simulations of (single and multiple) low-amplitude Keplerian RV signals induced by low-mass companions around M-dwarf primaries. The global performance of the three period search approaches is quite similar in the limit of an idealized, best-case scenario (single planets, circular orbits, white noise). However, GLS, BGLS and FREDEC are not equivalent when it comes to the correct identification of more complex signals (including correlated noise of stellar origin, eccentric orbits, multiple planets), with variable degrees of efficiency loss as a function of system parameters and degradation in completeness and reliability levels. The largest discrepancy is recorded in the number of false detections: the standard approach of residual analyses adopted for GLS and BGLS translates in large fractions of false alarms ($\sim30\%$) in the case of multiple systems, as opposed to $\sim10\%$ for the FREDEC approach of simultaneous multi-frequency search. Our results reinforce the need for the strengthening and further development of the most aggressive and effective {\it ab initio} strategies for the robust identification of low-amplitude planetary signals in RV datasets, particularly now that RV surveys are beginning to achieve sensitivity to potentially habitable Earth-mass planets around late-type stars., 11 pages, 10 figures, accepted for publication in MNRAS