Dr. Kai Hou (Gordon) Yip, Ariel Data Challenge Lead at UCL Physics and Astronomy, said: "We are excited to see the innovative solutions that the global data science community can bring to this formidable task."
This competition is a collaborative effort led by the UCL Centre for Space Exochemistry Data, with partners including Centre National D'etudes Spatiales, Cardiff University, Sapienza Universita di Roma, and Institut Astrophysique de Paris.
The competition is sponsored by Centre National d'Etudes Spatiales, in collaboration with the Kaggle Competitions Research Program. Support also comes from various 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."
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 understanding of the cosmos, challenging ideas about the solar system, Earth's uniqueness, and the potential for life elsewhere. As of now, astronomers have identified over 5,600 exoplanets. However, detection is just the beginning; scientists aim to understand these worlds by studying their atmospheres.
The European Space Agency's Ariel Space Mission, led scientifically by UCL's Professor Giovanna Tinetti, is set to launch in 2029. It will conduct one of the largest surveys of exoplanets, observing the atmospheres of about 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 determining their properties is a major challenge. These signals make up only a tiny fraction of the starlight from the planetary systems and are often distorted by instrument noise.
Professor Ingo Waldmann, Ariel Data Challenge co-lead at UCL Physics and 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 aims to overcome noise sources such as "jitter noise" from spacecraft vibrations. This noise, along with other disturbances, complicates the analysis of spectroscopic data used to study exoplanet atmospheres.
With support from the DiRAC HPC Facility, mission scientists have created the most accurate representation of Ariel observations to date. These are based on Ariel's payload design and include noise effects modeled from data obtained by the James Webb Space Telescope.
Scientists involved in the Ariel mission are now looking for new methods to push the boundaries of current data analysis approaches. They seek innovative solutions to effectively suppress these noise sources and extract critical signals from exoplanet atmospheres.
The Competition
The competition is open now until late October. Winners will be invited to present their solutions at the NeurIPS conference, with cash prizes available for the top six solutions.
This will be the fifth installment of the Ariel Machine Learning Data Challenge, following four competitions over the past five years. The Ariel Data Challenge attracts around 200 participants annually from across the world, including entrants from leading academic institutes and AI companies.
This challenge and its predecessors focus on making exoplanet research more accessible to the machine learning community. The challenge is not designed to definitively solve the mission's data analysis issues but provides a forum for discussion, encourages future collaborations, and helps the Ariel team prepare with the best possible data analysis methods by the time the mission launches.
More details about the competition and how to take part can be found on the Ariel Data Challenge website.
Related Links
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