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Modeling of exoplanetary magnetic fields and aurorae

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Aegis

This is a comprehensive repository containing everything done as part of the research project run by Asaf Kaya and Dr. Tansu Daylan on the Cyclotron Maser Instability (CMI) driven radio emissions from exoplanets. The codes use scientific knowledge acquired from various research papers and books.

The most essential prior file that needs to exist in the same directory with the python scripts is a .csv file containing data from NASA Exoplanet Archive with the required parameters. The name of this file is "NASA[sample].csv", where [sample] is usually the date the data was downloaded.

Python Scripts

The Modules

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. Both of these modules are essential to be able to run the main prediction script.

The Wind 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 file "wind_info[sample].date"

The Main Prediction Script

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 NASA Exoplanet Archive, in order to quantify uncertainty in the results. The script requires the existence of the exoplanet archive table in .csv format.

Extra Visual Routines

The programs "parker_spiral.py", "sketch.py" and "visibility.py" are used to create three of the figures in the paper: the perpendicular component of the IMF in the orbit of an exoplanet (currently tau Boo b), the schematic drawing of a magnetized exoplanet, and the visibility figure containing all-sky maximum elevation and time-spent-above-20-degrees maps for the considered telescopes, 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 currently "taub_wind.npz". The second is an independent script, while the latter 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"

Obsolete Scripts

The Scaling Script

The Python script "scaling.py"" serves the purpose of visualizing the scaling of radio luminosity of exoplanets with some of their parameters. These are expected not to have any direct contribution to any research paper that might be published.

Expectation Script and the GUI

The scripts "synthetic_predictions.py" and "GUI_for_predictions.py" deal with a generated sample of exoplanets. The former is the main script that generates a random sample of exoplanets by randomly assigning values drawn from specific probability distributions to various characteristics of exoplanets. It then plots the expectations of CMI radiation frequency and radio brightnesses of the drawn sample.

Output Files

The resulting radio flux densities and maximum emission frequencies from "exoplanet_predictions.py" are written out in two manners. First, the potentially visible candidates 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 "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 four different files that are subsets of this file divided from the expected frequencies.

Known Problems

Known problems of the project are summarized in the markdown file "known_problems.md".

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