Exoplanets, Where Do We Fit In?
By: Arnoud Buzing
Tags: Computational Thinking, Astronomy, Exoplanets, Data Science, Wolfram

Exoplanets, Where Do We Fit In?

Computational Thinking With a Dash of Wolfram Language

Photo by Greg Rakozy on Unsplash

It has only been about 30 years since the first planet outside our own solar system, an exoplanet, was confirmed. The chronology of exoplanet discovery is quite complex due to the fact that earlier observations, one as early as 1917, were not initially recognized as evidence of an exoplanet.

After a period of Earth-based and Hubble telescope discoveries, a space telescope, named Kepler, dedicated to the task of finding exoplanets was launched in 2009. Pointed permanently to just a tiny part of the night sky, it patiently recorded light coming from over 500,000 stars for several years.

Photo by Greg Rakozy on Unsplash

During its roughly 10 year mission, it detected over 2,600 new exoplanets. That, together with other discoveries, brings the total of known exoplanets to a little over 4,000. And many of these exoplanets have estimates for their orbital period, their mass, their radius, and so on. In this post, I am focussing on their masses and radii and comparing them to the planets in our solar system.

Obtaining Exoplanet Data

In the Wolfram Language, these exoplanets are directly accessible from the programmatic interface to Wolfram Knowledgebase. In the notebook interface, you can access any entity, not just exoplanets, with a nifty user interface:

(image by author)

The final result above can also be represented in a plain-text code form. They are exactly the same thing:

EntityClass["Exoplanet", All]

We can obtain the masses and radii for each planet with a simple query:

data = ExoplanetData[

EntityClass["Exoplanet", All], {

EntityProperty["Exoplanet", "Mass", {"Uncertainty" -> "Around"}],

EntityProperty["Exoplanet", "Radius", {"Uncertainty" -> "Around"}]

}, "EntityAssociation"]

Many exoplanets do not have an estimate for their mass and/or radius, and the ones that do often have significant uncertainties attached to them. A random sample of ten rows of the data shows what the typical situation looks like:

(image by author)

After filtering out the cases with either missing mass or radius, we get a nice clean dataset (another random sample of ten rows is shown):

(image by author)

Plotting the Data

It is now quite easy to plot the mass versus the radius for each exoplanet. The extra thin vertical and horizontal lines indicate the uncertainty for each exoplanet:

(image by author)

It is not unsurprising to see a large number of bigger exoplanets. Bigger planets are easier to spot and so more of them have been detected.

To compare these exoplanets with the planets in our solar system, we need to get them, and their masses and radii, first:

EntityValue[

EntityClass["Planet", All],

{"Mass", "Radius"},

"EntityAssociation"

]

Using the additional data, we can now repeat the same plot but with the solar system planets included:

(image by author)

This is quite interesting. It shows that there are quite a few exoplanets that are even bigger than Jupiter. There is an upper limit of course because at some size a very large planet will become a very small star.

On the other end of the exoplanet spectrum, it is clear we have not found many very small planets. Even Mars sits outside the main cluster of detected exoplanets. Hopefully one day we will find lots of those too!