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The Ariel Data Challenge 2022 is now open!
The Ariel Data Challenge 2022, which launches on 30th June, is inviting AI and machine learning experts from industry and academia to help astronomers understand planets outside our solar system, known as exoplanets.
Ariel will study the light from each exoplanet’s host star after it has travelled through the planet’s atmosphere in what is known as a spectrum. The information from these spectra can help scientists investigate the chemical make-up of the planet’s atmosphere and discover more about these planets and how they formed.
Scientists involved in the Ariel mission need a new method to interpret these data. Advanced machine learning techniques could help them to understand the impact of different atmospheric phenomena on the observed spectrum.
The Ariel Data Challenge calls on the AI community to investigate solutions. The competition is open from 30th June to early October.
Participants are free to use any model, algorithm, data pre-processing technique or other tools to provide a solution. They may submit as many solutions as they like and collaborations between teams are welcomed.
For the first time, this year the competition is also offering 20 participants access to High Powered Computing resource through DiRAC, part of the UK’s Science and Technology Facilities Council’s computing facilities.
To continue reading the full Ariel press release, click HERE
The paper is a collective study of updated ephemerides for 180 planets which are included in the Mission Reference Sample of the Ariel space mission. The paper has 105 co-authors including professional, amateur Astronomers and also university and high school students.
The UCL team, led by Dr Ahmed Al-Refaie, has worked hard to bring new groundbreaking features and performance improvements to the community. TauREx 3.1 features the new plugin system, allowing anyone to enhance and grow the framework with minimal effort. Accompanied with this release, TauREx 3.1 has plugins for blazing-fast GPU modelling, new chemistries, samplers, opacities and even other spectral codes!
Article: https://arxiv.org/abs/2110.01271
PyPi: https://pypi.org/project/taurex/
Github: https://github.com/ucl-exoplanets/TauREx3_public
Winners announced for the machine vs stellar and instrument noise data challenge
Artificial intelligence (AI) experts from around the world have been competing for the opportunity to help astronomers to explore planets in our local galactic neighbourhood.
The European Space Agency’s Ariel telescope, which launches in 2029, will study the atmospheres of around 1000 planets outside our solar system, known as exoplanets. Observing faint signals to measure the make-up of exoplanet atmospheres is incredibly challenging and is made even more so by other signals the instrument may pick up. The effect of star activity, like sun spots, and even the noise of the spacecraft itself can obscure the information scientists receive from Ariel.
The Ariel Machine Learning Data Challenge, sponsored by Spaceflux Ltd, was set to harness the expertise of the artificial intelligence community to help disentangle this unwanted noise from the light filtering through exoplanet atmospheres. Over 110 teams from around the world participated with 35 teams submitting viable solutions. The teams represented a mix of academia and AI companies.
The competition winners, ML Analytics, an artificial intelligence company in Portugal, and a team from TU Dortmund University in Germany were able to achieve highly accurate solutions for even the most difficult to observe planets.
To continue reading the full press release, please click here.