Studying extrasolar planets and ultimately searching for habitable worlds beyond our solar system is one of the most exciting frontiers in modern science, but a major obstacle stands in the way of unlocking the secrets of exoplanet atmospheres.
The Ariel Data Challenge 2024 is calling all data scientists, astronomers, and AI enthusiasts to help tackle one of astronomy's most complex and important data analysis problems—extracting faint exoplanetary signals from noisy space telescope observations.
The NeurIPS 2024, a world-renowned machine learning conference, will feature an exciting competition based on the Ariel Space Mission. This contest offers participants a unique chance to contribute to cutting-edge research in the fascinating field of exoplanet atmospheres. With a substantial prize pool of $50,000 USD at stake, the competition aims to attract top talent and innovative solutions.
Dr. Kai Hou (Gordon) Yip, Ariel Data Challenge Lead, UCL said:
“We are excited to see the innovative solutions that the global data science community can bring to this formidable task."
This groundbreaking challenge has been made possible through a collaborative effort led by UCL Centre for Space Exochemistry Data, bringing together an impressive international team of academic partners including Centre National D'études Spatiales, Cardiff University, Sapienza Università di Roma, and Institut Astrophysique de Paris. The competition is generously sponsored by Centre National d'Etudes Spatiales, in collaboration with Kaggle Competitions Research Program. It also benefits from the support of a consortium of leading space agencies and institutions, including the UK Space Agency, European Space Agency, STFC RAL Space, and STFC DiRAC HPC Facility.
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."
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, we are aware of over 5,600 exoplanets. However, detecting these worlds is only the initial step; we must also comprehend and characterise their nature by studying their atmospheres.
The European Space Agency's Ariel Space Mission 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.
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 James Webb Space Telescope.
Scientists involved in the Ariel mission now seek novel methods to push the boundaries of current data analysis approaches - innovative solutions that can effectively suppress these noise sources and extract vital signals from exoplanet atmospheres.
The competition
The competition is open now until late October. Winners will be invited to present their solutions at the prestigious NeurIPS conference, with cash prizes available for the top six solutions.
This will be the fifth instalment of the Ariel Machine Learning Data Challenge, following four very successful competitions in the past five years. The Ariel Data Challenge attracts around 200 participants from across the world every year, including entrants from leading academic institutes and AI companies.
This challenge and its predecessor took a bite-sized aspect of a larger problem to help make exoplanet research more accessible to the machine learning community. The challenge is not designed to definitively solve the data analysis issues faced by the mission outright but provides a forum for discussion, and to encourage future collaborations, and to help the Ariel team to be prepared with the best possible data analysis methods by the time the mission flies.
More details about the competition and how to take part can be found on the Ariel Data Challenge website.
Artist's impression of the Ariel spacecraft in space. Credit: ESA / STFC RAL Space / UCL / UK Space Agency / ATG Medialab
