Using IDL Helps Researchers Understand Earth-Like Planets in Other Solar Systems
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Customer Challenge
Researchers at the University of Colorado needed a data visualization and analysis solution that would integrate with the department's legacy code, and that would help them create simulations of planetary migration patterns.
Solution Achieved
Growing evidence that planets similar to the Earth exist in other planetary systems precipitated a ground-breaking study by scientists at the University of Colorado (CU) and Penn State who wanted to learn how those planets formed, and if the planets might possibly contain life forms.
The team of researchers focused on stars with “hot Jupiters”, giant planets orbiting very close to their stars, with scorching surface temperatures. Hot Jupiters are thought to form much farther away from their stars and migrate inward to their final orbits. This migration takes the giant planets right through the region where rocky, Earth-like planets can form. So, it had previously been assumed that the giant planet's migration would disrupt the formation of planets that might be suitable for life.
The team ran computer simulations of the growth of rocky planets during and after the migration of a giant planet on its way to becoming a hot Jupiter. They found that, contrary to popular belief, planets like the Earth can form after the giant planet has migrated through. These rocky planets can have properties similar to the Earth. One aspect of these planets, however, is different: during their growth they tend to accumulate a very large amount of water. Enough water, in fact, that the surfaces of these planets are probably covered in miles-deep oceans. So, planetary systems with hot Jupiters can contain Earth-like planets, but they are likely to be “water worlds”.
As part of the research team, Sean Raymond of CU used IDL® as his primary research tool. Raymond learned to use IDL during graduate work at the University of Washington because instructors and students considered it the best tool for performing complex computational analyses and creating visualizations – such as plots - for large datasets. And, because IDL was easy to learn, students could spend more time on their research and less time learning to program to see their results. “The biggest thing was that it was easy and quick to learn, so there wasn’t this big learning curve to begin doing our work,” said Raymond. “The vast majority of Graduate students used IDL.”
In the case of the Hot Jupiter project, Raymond and researchers from universities around the country conducted the simulations with existing data to determine how these enormous planets’ migration patterns affected the formation of planets around them. The process involved running simulations with a Fortran code, and then analyzing the output with IDL, where they could validate results and create plots.
Using IDL, they could easily create new code to quickly analyze huge arrays and datasets. “I write a lot of my functions on the fly,” said Raymond. “But I also keep a lot of recursive functions in my own library that I’ve written over the years. I used a lot of those in the analyses.” IDL’s array-based architecture allowed Raymond to use his recursive functions – on many arrays and eliminated the need for repetitive, time-consuming programming.
According the Raymond, the result of the team’s research indicates that Hot Jupiters actually allow for – not inhibit - the development of ocean-covered Earth-like planets. Now, scientists are eager to conduct further research based on their findings, and hope that they will find that other solar systems host planets similar to Earth with bodies of water and other life-sustaining factors. “Now, we need to link all of the different aspects of what we already know about geophysics, planetary science, and astronomy – and try to understand how they fit together in early planet formation.
Benefits
- They can now analyze the output of simulations and create intuitive visualizations that allow them to analyze planetary migration patterns.
- With IDL, the team could integrate legacy code written in other development languages with new IDL routines, saving time and money over rewriting scores of usable code.
- IDL was the best tool for performing complex computational analyses and creating visualizations – such as plots – for large datasets