Overview

We trained a machine learning model by using mixture density network (MDN) algorithm to quickly and efficiently predict the interior structure of rocky exoplanets with masses between 0.1 and 10 Earth masses.

Quick Start

Step 1:

Fork and clone a copy of the Rocky_Exoplanets repository on to your local machine.

Step 2:

Download Anaconda and install it on your machine. Create a conda environment called Rocky_Exoplanets and install all the necessary dependencies:

$ conda create -n Rocky_Exoplanets pip python jupyter tensorflow=1.15

Step 3:

Activate the Rocky_Exoplanets environment:

$ conda activate Rocky_Exoplanets

Step 4:

Change into your local copy of the Rocky_Exoplanets repo:

$ cd /you own path/Rocky_Exoplanets

Step 5:

Install the requirments for predicing in the current Conda environment:

$ pip install -r requirements.txt

Step 6:

Open Jupyter Notebook and load the file MDN_prediction.ipynb:

$ jupyter notebook

At this point you are ready to start investigating the interiors of rocky exoplanets!