In this work, we developed a simulation framework that processes pre-simulated stellar RV curves, accounting for oscillations, granulation, and active regions and injects synthetic planetary signals generated by the Bern model. The framework then samples the RV time series according to a realistic observational strategy and adds observational effects such as photon and instrumental noise. Finally, a planet recovery algorithm based on periodogram analysis and iterative signal subtraction is applied to compute planetary detection rates.

These detection rates are subsequently compared to those derived in Mayor et al. 2011 revealing differences in sensitivity, particularly for smaller planets and shorter orbital periods. Notably, our method shows higher detection rates for planets with larger RV amplitudes (K > 1 m·s⁻¹), and occasionally detects low-mass planets (< 1 Earth mass) under favorable conditions. In contrast, Mayor et al.'s approach tends to outperform our method in the low-amplitude regime (K < 1 m·s⁻¹), likely due to the presence of instrumental noise in our model.