Aegis is a repository that predicts the radio emission from exoplanets driven by Cyclotron Maser Instability (CMI) as a probe of planetary magnetism.
The required packages are listed in "environment.yml" and "requirements.txt".
You need to create a .csv file containing data from the NASA Exoplanet Archive with the required parameters. The name of this file is "NASA[sample].csv", where [sample] is usually the date (DDMM) the data was downloaded.
The code is written entirely in Python.
The script "radio_module.py" provides a new module with an exoplanet class and various functions that are used throughout the project. The script "rotation_script.py" provides an additional module to randomly sample spin angular momenta of exoplanets from the solar system distribution, contained in "rotation.csv". Both of these modules are essential to run the main prediction script.
"wind.py" imports data from a .csv file obtained from the NASA Exoplanet Archive with the required parameters, whose name is specified in the script. It calculates the stellar wind temperatures and speed at the orbit of the exoplanets in the sample, assuming isothermal Parker wind (Parker 1958). The results are written out to be used in the main prediction script into the file "new_wind_info[sample].date".
The main script that predicts exoplanetary emission characteristics is the file "exoplanet_predictions.py". The script implements a Monte-Carlo error propagation method to be used on exoplanets selected from the NASA Exoplanet Archive to quantify uncertainty in the results. It also creates distribution histograms of the initial parameters in the sample and the posterior distributions of emission characteristics of a select group of targets.
The script requires the exoplanet archive table to be in .csv format. The results presented in (Kaya & Daylan, accepted to ApJ) are obtained with a Monte Carlo iteration count of 10,000, although this number can be changed at will while running the script for the sake of computation load. Running this script creates a number of result files summarized in the Output Files section. It also creates "df1.csv" and "all.npz", which are used to make the figures by "plotter.py".
"plotter.py" is the script creating Figures 7 and 8 of the manuscript, displaying the distribution of emission characteristics among the planets in our sample. It requires the existence of "df1.csv" and "all.npz", generated by "exoplanet_predictions.py". It also requires files "nenufar.npz" and "sensitivities.csv" to plot the sensitivity limits of NENUFAR and all the other telescopes. Both of these files are created externally.
The scripts "parker_spiral.py", "sketch.py", "visibility.py", and "radius_valley.py" are used to create four of the figures in the manuscript. In the paper: the perpendicular component of the IMF in the orbit of an exoplanet (tau Boo b), the schematic drawing of a magnetized exoplanet, the visibility figure containing all-sky maximum elevation and time-spent-above-20-degrees maps for the considered telescopes, and the last figure in the paper that illustrates the distribution of radii and magnetic field strengths of targets, respectively. The first one requires the existence of wind data from an exoplanet, currently tau Boo b, to be present in the directory in .npz format. This file is presently "taub_wind.npz". The second is an independent script, while the third requires the existence of the file "obs_table.csv" in the same working directory. This file is created within "extract.py", which extracts the necessary information for the opportune targets determined from "exoplanet_predictions.py". Finally, "radius_valley.py" requires the existence of the table "all.csv" in the folder "Output Tables".
"does_it_make_a_difference.py" quantifies the effect of employing a stochastic Monte Carlo approach to our final results, providing an important justification for the argument that our results may differ from others in the literature because of its probabilistic approach.
The resulting radio flux densities and maximum emission frequencies from "exoplanet_predictions.py" are written out in two ways. First, the potentially visible candidates were determined from the magnetic and kinetic RBLs, and the candidates determined from an integration of both RBLs are separated, sorted by their names, frequencies, and flux densities separately into nine .txt files in the folder "Old Result Tables". Secondly, only the "both" RBL methods results for all exoplanets are written out to csv files with the uncertainties included in "Output Tables". In this case, the results for all exoplanets can be found in "all.csv", while there also exist five different files that are subsets of this file, divided from the expected frequencies.
Finally, "extract.py" and "append.py" create the tables "final-table.csv" and "append.csv", respectively. These two files constitute the two tables that are displayed in the manuscript.
Known problems of the project are summarized in the markdown file "known_problems.md".
The development of aegis has been supported by the McDonnell Center for Space Sciences at Washington University in St. Louis.