EMPEROR I. Exoplanet MCMC parallel tempering for RV orbit retrieval

Abstract

We present EMPEROR, an open-source Python framework designed for efficient exoplanet detection and characterisation with radial velocities (RV). EMPEROR integrates Dynamic Nested Sampling (DNS) and Adaptive Parallel Tempering (APT) Markov Chain Monte Carlo (MCMC), supporting multiple noise models such as Gaussian Processes (GPs) and Moving Averages (MA). The framework enables systematic model comparison using statistical metrics, including Bayesian evidence ($\ln{\mathcal{Z}}$) and Bayesian Information Criterion (BIC), while providing automated, publish-ready visualisations. EMPEROR is evaluated across three distinct systems to assess its capabilities in different detection scenarios. Sampling performance, model selection, and the search for Earth-mass planets are evaluated in data for 51 Pegasi, HD 55693 and Barnard's Star (GJ 699). For 51 Pegasi, APT achieves an effective sampling increase over DNS by a factor 3.76, while retrieving tighter parameter estimates. For HD 55693 the stellar rotation $P_{\text{rot}}=29.72^{+0.01}_{-0.02}$ and magnetic cycle $P_{\text{mag}}=2557.0^{+70.1}_{-36.7}$ are recovered, while demonstrating the sensitivity of $\ln{\mathcal{Z}}$ to prior selection. For Barnard's star, several noise models are compared, and the confirmed planet parameters are successfully retrieved with all of them. The best model shows a period of 3.1536$\pm$0.0003~d, minimum mass of 0.38$\pm$0.03 M$_{\rm{\oplus}}$, and semi-major axis of 0.02315$\pm$0.00039~AU. Purely statistical inference might be insufficient on its own for robust exoplanet detection. Effective methodologies must integrate domain knowledge, heuristic criteria, and multi-faceted model comparisons. The versatility of EMPEROR in handling diverse noise structures, its systematic model selection, and its improved performance make it a valuable tool for RV exoplanetary studies.