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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.
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