Radial-Velocity Fitting Challenge. II. First results of the analysis of the data set
X. Dumusque, F. Borsa, M. Damasso, R. Diaz, P. C. Gregory, N.C. Hara,, A. Hatzes, V. Rajpaul, M. Tuomi, S. Aigrain, G. Anglada-Escude, A.S. Bonomo,, G. Boue, F. Dauvergne, G. Frustagli, P. Giacobbe, R. D. Haywood, H. R. A., Jones, M. Pinamonti, E. Poretti, M. Rainer

TL;DR
This paper compares methods for detecting low-mass exoplanets in radial velocity data by analyzing simulated datasets, highlighting the most effective approaches and establishing a reliable detection threshold.
Contribution
It introduces a standardized comparison of RV analysis methods using simulated data, identifying the most effective techniques and defining a detection threshold for low-mass planets.
Findings
Red-noise models and Bayesian frameworks improve detection accuracy.
Planets with K/N ratio above 7.5 are reliably detected.
False positives are minimal above the threshold.
Abstract
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…
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Taxonomy
TopicsStellar, planetary, and galactic studies · Statistical and numerical algorithms · Gamma-ray bursts and supernovae
