What would it take to hear an exoplanet's magnetic field? We use Aegis to predict radio emission driven by cyclotron maser instability (CMI), propagate uncertain system properties through stellar-wind and planetary-emission models, and compare the resulting frequency and flux distributions with telescope sensitivities. The predictions depend on the adopted wind, spin, and magnetic-field assumptions, which are worth inspecting before choosing a target.
The required packages are listed in environment.yml and requirements.txt.
Start with a NASA Exoplanet Archive table containing the required system parameters. Save it as NASA[sample].csv, where [sample] is usually the download date in DDMM format. The scripts use that sample label to connect their inputs and outputs.
rotation_script.py samples exoplanet spin angular momenta from the Solar System distribution in rotation.csv. The resulting spin distribution enters the planetary magnetic-field and radio-emission calculations.
wind.py calculates the stellar-wind temperature and speed at each planet's orbit with an isothermal Parker wind model (Parker 1958). It reads the NASA Exoplanet Archive sample and writes new_wind_info[sample].date for the radio-emission calculation.
exoplanet_predictions.py propagates uncertainties in NASA Exoplanet Archive measurements through the magnetic-field and radio-emission models with Monte Carlo sampling. It calculates predictive distributions of emission frequency and radio flux density for each target.
For a detected cyclotron-maser cutoff, magnetic_field_inference.py estimates the surface field from Aegis's cutoff-frequency relation and a Gaussian measurement likelihood. Install the dependencies in requirements.txt, then run the explicitly synthetic example with python examples/infer_magnetic_field.py. The fit uses a uniform magnetic-field prior and complements the forward Monte Carlo predictions with an observation-driven estimate.
The calculation requires the exoplanet archive table in comma-separated value format. We use 10,000 Monte Carlo samples. Reduce the count for faster exploratory calculations. The script also writes df1.csv and all.npz for the population figures. These draws propagate stated measurement uncertainties and the empirical Solar System spin distribution through a forward model. The cutoff fit above estimates a model parameter from an observed frequency.
plotter.py plots the Monte Carlo predictive emission-frequency and radio-flux distributions from df1.csv and all.npz. The nenufar.npz and sensitivities.csv inputs add the sensitivity limits of NenuFAR and other radio telescopes.
parker_spiral.py plots the interplanetary magnetic-field component at the orbit of tau Boo b from taub_wind.npz. visibility.py maps maximum elevation and time above 20 degrees for the considered telescopes using obs_table.csv. radius_valley.py compares the planetary radii and predicted magnetic-field strengths in Output Tables/all.csv. sketch.py draws the magnetized-exoplanet geometry.
does_it_make_a_difference.py quantifies how Monte Carlo uncertainty propagation changes the predicted radio-emission distributions relative to calculations based only on nominal input values.
exoplanet_predictions.py writes radio flux densities and maximum emission frequencies for the magnetic, kinetic, and combined radiometric Bode-law predictions. Output Tables/all.csv contains the combined predictions and uncertainties for every exoplanet. Five additional tables divide the targets by predicted frequency. The text tables under Old Result Tables sort potentially visible candidates by name, frequency, and flux density.
extract.py and append.py produce final-table.csv and append.csv, the two manuscript tables.
Known limitations are summarized in known_problems.md.
The development of aegis has been supported by the McDonnell Center for Space Sciences at Washington University in St. Louis.