Exoplanetary radio emission predictions and detectability in the SKA era

TL;DR

This paper uses machine learning and radiometric laws to predict exoplanetary radio emissions, identifying promising targets for SKA detection and emphasizing the importance of considering radio quenching effects.

Contribution

It introduces a reliable machine learning model for predicting exoplanet radio flux and frequency, aiding targeted observations with the SKA.

Findings

64 exoplanets could be detected by SKA

MASCARA-1 b is an excellent SKA-Low target

WASP-18 b is the most promising SKA-Mid candidate

Abstract

Radio observations provide a window into a planet's interior and play a crucial role in studying its atmosphere and surface, key factors to find potential habitability. The discovery of thousands of exoplanets, together with advances in radio astronomy through the Square Kilometre Array (SKA), motivates the search for planetary-scale radio emissions. Here, we employ the radiometric Bode's law (RBL) and machine learning techniques to analyze a dataset of 1330 confirmed exoplanets, aiming to estimate their potential radio emission. Permutation Importance (PI) and SHapley Additive exPlanations (SHAP) analyses indicate that a planet's mass, radius, orbital semi-major axis, and distance from Earth are sufficient to dependably forecast its radio flux and frequency. The random forest model accurately reproduces these radio characteristics, confirming its reliability for exoplanetary radio predictions. Considering observational constraints, we find that 64 exoplanets could generate signals detectable by the SKA, 52 of which remain observable in the intermediate AA* deployment. Among these, MASCARA-1 b stands out with a predicted flux of 7.209 mJy at 135.1 MHz, making it an excellent SKA-Low target. Meanwhile, WASP-18 b, with a flux of 18.638 mJy peaking at 812.9 MHz, is the most promising candidate for SKA-Mid. These results show that the SKA can detect gas giants, such as MASCARA-1 b (SNR>400) and WASP-18 b (SNR>4236), within feasible integration times. Additionally, we identify four candidates (HATS-18 b, WASP-12 b, WASP-103 b, and WASP-121 b) that are likely affected by radio quenching, highlighting the importance of considering this effect in target selection for observation campaigns.