Resources

Baseline model and Metric:

Please see the Baseline page for a fully model description. For a description of the competition scoring metric, please see the Scoring page

You can access the full baseline model and competition metric on our GitHub repository.

Slack channel and email:

Please see the Slack channel for discussions and Q&A’s, or contact us via email: exoai.ucl (AT) gmail.com

Associated Publications:

Codes:

Ariel Related Publications:

  • Tinetti et al. (2020): Ariel: Enabling planetary science across light-years, Ariel Definition Study Report. Reviewed by ESA Science Advisory Structure.
  • Edwards & Tinetti (2022): The Ariel Target List: The Impact of TESS and the Potential for Characterising Multiple Planets Within a System, Accepted in ApJ.
  • Edwards et al. (2019): An Updated Study of Potential Targets for Ariel, AJ 157 242.
  • Ariel official Website

Exoplanet Publications:

  • Changeat et al. (2022): Five Key Exoplanet Questions Answered via the Analysis of 25 Hot-Jupiter Atmospheres in Eclipse, ApJS 260 3.
  • Zhu & Dong (2021): Exoplanet Statistics and Theoretical Implications, ARA&A, 59, 291-336
  • Cho et al. (2021): Storms, Variability, and Multiple Equilibria on Hot-Jupiters, ApJL 913 L32
  • Venot et al. (2020): New chemical scheme for giant planet thermochemistry. Update of the methanol chemistry and new reduced chemical scheme, A&A, 634, A78, 17
  • Tsiaras et al. (2019): Water vapour in the atmosphere of the habitable-zone eight-Earth-mass planet K2-18 b, Nat. Ast. 3 1086–1091.
  • Madhusudhan (2019): Exoplanetary Atmospheres: Key Insights, Challenges, and Prospects, ARA&A, 57,617-663
  • Tsiaras et al. (2018): A Population Study of Gaseous Exoplanets, AJ 155 156.
  • Seager & Mallén-Ornelas (2003): A Unique Solution of Planet and Star Parameters from an Extrasolar Planet Transit Light Curve, AJ, 585, 2, 1038 - 1055.

Relevant ML Publications:

  • Yip et al. (2022): To Sample or Not To Sample: Retrieving Exoplanetary Spectra with Variational Inference and Normalising Flows, Submitted to ApJ.
  • Ardevol Martinez et al. (2022): Convolutional neural networks as an alternative to Bayesian retrievals, A&A 662, A108
  • Himes et al. (2022): Accurate Machine-learning Atmospheric Retrieval via a Neural-network Surrogate Model for Radiative Transfer, Planet. Sci. J. 3 91.
  • Yip et al. (2021): Peeking inside the Black Box: Interpreting Deep Learning Models for Exoplanet Atmospheric Retrievals, AJ 162 195.
  • Cobb et al. (2019): An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval, AJ 158 33.
  • Zingales & Waldmann (2018) : ExoGAN: Retrieving Exoplanetary Atmospheres Using Deep Convolutional Generative Adversarial Networks, AJ 156 268
  • Márquez-Neila et al. (2018): Supervised machine learning for analysing spectra of exoplanetary atmospheres,Nat. Ast. 2 719–724.