KOBEsim: improving RV detection through efficient scheduling

The KOBEsim algorithm is a Bayesian-based strategy for the detection of planets in radial velocity (RV) surveys written in Python. It is developed within the KOBE (K-dwarfs Orbited By habitable Exoplanets) experiment, aiming at maximizing the detection of rocky exoplanets potentially habitable orbiting K-dwarfs. After gathering the first data, KOBEsim targets the predominant orbital period and finds the optimum next observing date to maximize the efficiency of confirming or discarding that signal. This new approach has demonstrated to improve nearly 50 % the detection efficiency in comparison with a conventional strategy of monotonic cadence.