Exploring Exoplanet Atmospheres With AI

Dr. Caroline Harper, Head of Space Science, UK Space Agency, said: “By supporting this challenge, we aim to find new ways of using AI and machine learning to develop our understanding of the universe.

“Exoplanets are likely to be more numerous in our galaxy than the stars themselves and the techniques developed through this prestigious competition could help open new windows for us to learn about the composition of their atmospheres, and even their weather.

“The UK Space Agency’s investment in cutting-edge space science research is essential for supporting innovative missions like this, that can benefit people, businesses, and communities across the globe. We can’t wait to see the results.”

Dr. Theresa Rank-Lueftinger, Project Scientist for the ESA (European Space Agency) Ariel mission, said: “Every noisy signal from our space telescopes could hide the key to understanding remote atmospheres. Our job is to unlock that potential with innovative machine learning approaches. It will be amazing to see what the AI community comes up with!”

Understanding the Atmospheres of Exoplanets

The discovery of exoplanets has transformed our cosmic perspective, challenging conventional notions about the nature of the solar system, the Earth’s uniqueness, and the potential for life elsewhere.

As of today, astronomers are aware of over 5,600 exoplanets. However, detecting these worlds is only the initial step, as scientists seek to further comprehend and characterize the nature of these worlds by studying their atmospheres.

The European Space Agency’s Ariel Space Mission, whose scientific leadership is provided by UCL’s Professor Giovanna Tinetti, will be launched in 2029 and will complete one of the largest-ever surveys of these planets by observing the atmospheres of around one-fifth of the known exoplanets.

Paul Eccleston, Ariel Mission Consortium Manager, RAL Space, said: “It’s an exciting time for Ariel and for RAL Space involvement, where we’re due to start building the payload structural model in the coming months. It’s also a busy time for other parts of the consortium, including those that are pre-empting data challenges we might face after launch.

“The Ariel Data Challenge will be incredibly useful for us in this respect, but it’s also a great opportunity for participants to get involved and contribute to a very exciting mission. Good luck to those taking part!”

However, observing these atmospheres and deriving their properties is a formidable challenge. These atmospheric signals only account for a minute fraction of the starlight received from the planetary systems, and are regularly corrupted by instrument noise.

Professor Ingo Waldmann, Ariel Data Challenge co-lead based at UCL Physics & Astronomy, said: “Modern astrophysics poses big-data problems that can best, and sometimes only, be solved using modern AI techniques. This problem in particular lends itself to fresh approaches, and I am very excited to see what new solutions the AI community will come up with.”

The Ariel Data Challenge

The Ariel Data Challenge 2024 focuses on overcoming these noise sources, such as “jitter noise” caused by spacecraft vibrations. This noise, along with other disturbances, complicates the analysis of spectroscopic data used to study exoplanet atmospheres.

With support from DiRAC HPC Facility, mission scientists have meticulously produced the most accurate representation of Ariel observations to date, based on Ariel’s payload design and incorporating representative noise effects from in-flight data obtained by the Ariel Data Challenge website.