2023MNRAS.519.6210M

With over 5000 exoplanets currently detected, there is a need for a primary classification method to prioritize candidates for biosignature observations. Here, we develop a classification method to categorize rocky exoplanets based on their closest Solar system analogue using available data of observed stellar and planetary features, masses, and radii, to model non-thermal atmospheric escape, thermal atmospheric escape, and stellar irradiation boundaries. Applying this classification method to the 720 rocky exoplanets in our sample with uncertainties in planetary masses, radii, stellar temperatures, and fluxes propagated via a Monte Carlo model indicates that 22 per cent ± 8 per cent are Mercury analogues, 39 per cent ± 4 per cent are Mars analogues, 11 per cent ± 1 per cent are Venus analogues, 2 per cent ± 1 per cent are Earth analogues, and 26 per cent ± 12 per cent are without a known planetary counterpart in our Solar system. Extrapolating to conditions on LHS 3844b and GJ 1252b, our classification method gives results reasonably consistent with current observations. Subsequently, to demonstrate the functionality of this classification method, we plot our catalogued sample of exoplanets on an adjusted surface pressure versus temperature phase diagram, presenting more realistic estimates of the potential surface phases (gas, liquid, or ice). Our new classification method could help target selection for future exoplanet characterization missions.