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.
AEGIS_PATH points to the Aegis checkout used by the analysis scripts. When it is unset, Aegis uses the directory containing repository_paths.py. The checkout's data/ and visuals/ directories are ignored by Git for local inputs and generated figures.
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.
We publish five selected predicted targets in a searchable radio catalog. The page sorts by target, maximum frequency, or predicted flux and filters by name and frequency. These are model predictions, not detections.
To rebuild the site data from append_table.csv, run:
python publish_radio_catalog.py --check-physicalityThe catalog is published at https://astromusers.github.io/aegis/ when the GitHub Pages environment is enabled for this repository.
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 stellar_wind_predictions_[sample].tsv 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 cutoff-inference notebook compares
two seeded synthetic measurements by passing them through
infer_surface_field(). It displays the returned PCAT posterior intervals and
their distributions. These toy inputs exercise the likelihood and sampling
workflow; they are not radio observations or field measurements of real
exoplanets. The short chains illustrate the API and do not establish
convergence.
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.
For an interactive target view with hover values, uncertainty bars, and telescope sensitivity curves, install Bokeh and save the returned figure as HTML:
from bokeh.io import output_file, save
from plotter import df, scatter_plot_bokeh, x_err, y_err
output_file("visuals/aegis_radio_predictions_bokeh.html")
save(scatter_plot_bokeh(df, "both", y_err, x_err))outcome_dist_hists() and scatter_plot() use white backgrounds with black
axes by default. Pass typeplotback='dark' for black backgrounds with white
axes, including saved figures. They also inherit an enclosing TDpy
plot_style() theme without changing later plots.
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.