Romain Eltschinger | Searcharxiv

arXiv2026

Completeness and dynamical stability in exoplanet occurrence rates

Exoplanet population studies are often quantified through the "occurrence rate" of certain types of planet. This value can be calculated from a survey of a sample of stars by taking the "detection rate" and correcting with a completeness factor. This completeness is calculated via detection limits on each individual star, and can be calculated either individually for each detected planet or averaged in a certain area of parameter space. We test whether either the average completeness or individual completeness performs better than the other. We also test whether including dynamical stability constraints in the detection limits improves the occurrence rate results. We use synthetic sets of exoplanetary systems and calculate occurrence rates for each and compare to what the value would be with perfect information. We compare how close each method is to the ground truth to assess which is most suitable. Overall, in our synthetic sets, the average completeness is more reliable though it can be a problem if there is a sharp change in the underlying population within the area of parameter-space considered. We find that using dynamical stability conditions causes a bias in the occurrence rate and do not recommend using these.

astro-ph.EP↗

arXiv2025

Earth-like planet predictor: A machine learning approach

Searching for planets analogous to Earth in terms of mass and equilibrium temperature is currently the first step in the quest for habitable conditions outside our Solar System and, ultimately, the search for life in the universe. Future missions such as PLATO or LIFE will begin to detect and characterise these small, cold planets, dedicating significant observation time to them. The aim of this work is to predict which stars are most likely to host an Earth-like planet (ELP) to avoid blind searches, minimises detection times, and thus maximises the number of detections. Using a previous study on correlations between the presence of an ELP and the properties of its system, we trained a Random Forest to recognise and classify systems as 'hosting an ELP' or 'not hosting an ELP'. The Random Forest was trained and tested on populations of synthetic planetary systems derived from the Bern model, and then applied to real observed systems. The tests conducted on the machine learning (ML) model yield precision scores of up to 0.99, indicating that 99% of the systems identified by the model as having ELPs possess at least one. Among the few real observed systems that have been tested, 44 have been selected as having a high probability of hosting an ELP, and a quick study of the stability of these systems confirms that the presence of an Earth-like planet within them would leave them stable. The excellent results obtained from the tests conducted on the ML model demonstrate its ability to recognise the typical architectures of systems with or without ELPs within populations derived from the Bern model. If we assume that the Bern model adequately describes the architecture of real systems, then such a tool can prove indispensable in the search for Earth-like planets. A similar approach could be applied to other planetary system formation models to validate those predictions.

astro-ph.EP↗