Exoplanetary radio emission predictions and detectability in the SKA era

TL;DR

This work tackles the challenge of predicting exoplanetary auroral radio emission by combining radiometric Bode's law (RBL) with a machine-learning augmentation. Using $P_{ ext{rad}}$ and $f_c$–dependent physics, the authors train two random forest regressors to reproduce RBL-derived flux $ ext{Phi}$ and frequency $f_c$ from a compact feature set $(M_p, R_p, a, D)$, achieving high fidelity with $R^2$ values of $0.911$ for $ ext{Phi}$ and $0.993$ for $f_c$. Extending predictions to 1330 exoplanets and applying realistic SKA observing constraints (ionospheric cutoff $ ext{f}_c>10$ MHz and declination $ ext{δ}<+30^ ext{o}$), they identify 248 viable targets, with 58 in SKA-Low and 69 in SKA-Mid, and they quantify detectability under 5σ imaging sensitivities for AA4 and AA* configurations. The results highlight promising targets such as MASCARA-1 b and WASP-18 b, reveal the impact of radio quenching in several candidates, and underscore the need to integrate quenching effects in target selection for SKA campaigns. Overall, the paper demonstrates a practical, data-driven path to prioritizing exoplanetary radio observations and lays groundwork for maximizing SKA's potential to probe planetary magnetospheres and habitability.

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.

Figures (8)

• Figure 1: The planetary radius ($R_{p}$) plotted as a function of planet's mass ($M_{p}$) and colour coded by orbital semi-major axis ($a$), for a sample of 1259 planets. Planets with radii determined using the mass-radius relation from 2024AA...686A.296M (see equation \ref{['eq1']}) are highlighted with red-bordered circles. These data points follow the mass-radius trend seen among the observed planetary samples. Blue lines represent kernel density estimate contours. The positions of Earth and Jupiter within this parameter space, as two radio-emitting planets in the solar system, are depicted.
• Figure 2: Feature importance analysis for predicting exoplanetary radio flux (top panels) and characteristic frequency (bottom panels), showing mean permutation importance (left panels) and mean absolute SHAP values (right panels). The error bars represent the standard deviation of the permutation importance across 1000 random shuffles of each feature. For radio flux, the three most influential features are planetary radius ($R_{p}$), Earth-star distance ($D$), and orbital semi-major axis ($a$), whereas for characteristic frequency, planetary mass ($M_{p}$) is the most important, followed by planetary radius ($R_{p}$) and orbital semi-major axis ($a$). The stellar radius ($R_{s}$), mass ($M_{s}$), and effective temperature ($T_{\text{eff}}$), are less important in predicting both radio flux and frequency.
• Figure 3: Comparison of the radio flux predicted by the random forest model ($\Phi_{RF}$) with the flux calculated using the radiometric Bode’s law ($\Phi_{RBL}$) in the upper panel, along with the residuals shown in the lower panel.
• Figure 4: Comparison of the characteristic frequency predicted by the random forest model ($f_{c,\;RF}$) with the frequency calculated using the radiometric Bode’s law ($f_{c,\;RBL}$) in the upper panel, along with the residuals shown in the lower panel.
• Figure 5: Histograms of exoplanet properties, grouped by their expected radio emission frequencies ($f_{c}$): below 10 MHz (grey) and above 10 MHz (blue). The left panel shows the probability density function of the predicted radio flux ($\Phi$), while the middle and right panels show the distributions of planetary masses ($M_{p}$) and radii ($R{_p}$), respectively. Vertical lines mark the values for Earth (red, dotted) and Jupiter (green, solid) for reference. Exoplanets with $f_{c}>10$ MHz generally have higher masses and larger radii than those with $f_c<10$ MHz, but tend to produce comparatively weaker radio fluxes.