Dr. Brett Addison's Data Science Projects & Blog

Astrophysicist | Data Scientist

Welcome to my data science page, the place where I discuss topics and projects in the field of data science that I am currently working on or have worked on in the past. I am currently in the process of transitioning from astronomy into data science.

Predicting the Orbital Obliquities of Exoplanets Using Machine Learning

Part 2: Obtaining & Cleaning Datasets & Fitting First Random Forest Regression Model

Posted by Brett Addison on 10 January 2025 • Topics: Machine Learning, Random Forest Regression, Python, Visualization, Exoplanets • 5-minute read

Illustration of a random forest model consisting of N number of decision trees, with each tree consisting of M number of branches (or nodes), similar to how a forest consists of many trees and each tree consists of many branches. The nodes within each decision tree are used to make a prediction, and all of the predictions are averaged together to produce a final result. The red path taken in each decision tree is where specific criteria are met that result in a prediction. Image credit: Brett Addison.

In this project, I applied machine learning to explore an interesting question in astronomy: Can we predict the orbital obliquities of exoplanets using their host stars' properties and planetary system features? Orbital obliquity—the angle between a planet's orbital plane and the equator of its host star—offers crucial insights into planetary formation and evolution. Here's how I approached this challenge using a random forest regression model.

Data Collection and Pre-processing

The first step involved obtaining data from two databases: the catalog of the physical properties of transiting planetary systems (TEPCat) and the NASA Exoplanet Archive. These sources provided the required properties of the exoplanets and their host star's to build a machine learning model, including, for example, the masses, radii, orbital distances, and orbital obliquities. However, the sample size of planets with measured orbital obliquities was small and the measurements came with significant uncertainties. Additionally, the dataset is somewhat imbalanced, there are more than double the number of planets on low obliquity orbits compared to high obliquity orbits as shown in the figure below.

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Predicting the Orbital Obliquities of Exoplanets Using Machine Learning

Part 1: Background on Exoplanets and Orbital Obliquity

Posted by Brett Addison on 5 January 2025 • Topics: Exoplanets, Machine Learning, Random Forest Regression & Classification • 5-minute read

Artist impression of the retrograde orbit of the ultra-hot Jupiter TOI-1431b/MASCARA-5b, spin-orbit angle measured in Stangret et al (2021) and discovery paper by Addison et al (2021). Image credit: Brett Addison.

What are Exoplanets?

Extrasolar planets (exoplanets) are planets that orbit stars outside of the Solar System. I have been truly fascinated by the sheer diversity of exoplanets that have been discovered in my field of research over the past three decades. Nearly all of the over 5,000 exoplanets discovered to date look nothing like the planets we have in the Solar System (check out the NASA Exoplanet Archive for the latest tally)! These planets range from scorching "hot Jupiters"--Jupiter-sized planets that whip around their star in only a few days--to super-Earths and mini-Neptunes (planets between the size of Earth and Neptune), and even planets that orbit around their parent star backwards (retrograde). This incredible variety contrasts starkly with the orderly configuration of the Solar System, where planets follow near-coplanar orbits aligned with the Sun's equator.

This raises an intriguing question: Is the Solar System unique?

How Are Exoplanets Detected?

To address this question, I will first cover the two primary methods of discovering exoplanets and their detection biases. These two methods are the transit method and the radial velocity method (see the excellent review of exoplanet detection methods by Jason Wright and Scott Gaudi).