The ExoAI project's outstanding achievement includes the detection of water vapour in the atmosphere of a temperate super-Earth/warm-Neptune, K2-18b (Tsiaras et al. 2019, Nature Astronomy). This discovery is crucial for understanding potentially habitable planets in our solar system. The project has also published studies on hot-Neptunes and hot-Jupiters, establishing a catalogue of uniformly analysed exoplanets and shedding light on planet formation and atmospheric parameters (Tisaras et al. 2018, Changeat et al. 2022, Edwards et al. 2022).
To improve data analysis, ExoAI pioneered the use of deep learning techniques like LSTM neural networks and transformers (Morvan et al. 2020, 2021, 2022). These algorithms efficiently extract faint planetary signals from instrumental noise, surpassing traditional approaches. The TauREx 3 framework (Al-Rafaie et al. 2020, 2021, 2022), a sophisticated atmospheric modelling tool, enables faster analysis and uniformity across the exoplanet community for better population insights. It has led to over 250 publications in the field and was downloaded 64,000 times.
Moreover, the project explores deep learning's potential in the retrieval process, accelerating data analysis and allowing incorporation of more complex models (Zingales & Waldmann 2018, Yip et al. 2021, 2023). We have also conducted the first study of Explainable AI (XAI) in how and if deep learning algorithms learn the underlying atmospheric physics from example (Yip et al. 2021). This is an important fundamental stepping stone for the field as we demonstrated that learning does indeed occur in deep learning approaches. The ExoAI project's pioneering efforts in data analysis and atmospheric modeling open new frontiers in exoplanet research, advancing our knowledge of distant worlds and their potential for habitability.
Beyond planetary science, ExoAI's algorithms find diverse applications, from methane detection on Mars to identifying ammonia clouds on Saturn (Waldmann & Griffith 2019, Nature Astronomy), to detecting illegal fishing activity in South East Asia and to detecting Satellites orbiting our Earth (ERC-PoC 2023). Our cross-disciplinary applications demonstrate the versatility and impact of AI-driven solutions in diverse scientific domains.
