Optimizing exoplanet atmosphere retrieval using unsupervised machine-learning classification

@article{Hayes2020OptimizingEA,
  title={Optimizing exoplanet atmosphere retrieval using unsupervised machine-learning classification},
  author={J. J. Hayes and E. Kerins and S. Awiphan and I. McDonald and J. S. Morgan and P. Chuanraksasat and S. Komonjinda and N. Sanguansak and P. Kittara},
  journal={Monthly Notices of the Royal Astronomical Society},
  year={2020},
  volume={494},
  pages={4492-4508}
}
  • J. J. Hayes, E. Kerins, +6 authors P. Kittara
  • Published 2020
  • Physics
  • Monthly Notices of the Royal Astronomical Society
  • One of the principal bottlenecks to atmosphere characterisation in the era of all-sky surveys is the availability of fast, autonomous and robust atmospheric retrieval methods. We present a new approach using unsupervised machine learning to generate informed priors for retrieval of exoplanetary atmosphere parameters from transmission spectra. We use principal component analysis (PCA) to efficiently compress the information content of a library of transmission spectra forward models generated… CONTINUE READING
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