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  • 标题:ExoGAN: Retrieving Exoplanetary Atmospheres Using Deep Convolutional Generative Adversarial Networks
  • 本地全文:下载
  • 作者:Tiziano Zingales ; Ingo P. Waldmann
  • 期刊名称:The Astronomical journal
  • 印刷版ISSN:0004-6256
  • 电子版ISSN:1538-3881
  • 出版年度:2018
  • 卷号:156
  • 期号:6
  • 页码:1-14
  • DOI:10.3847/1538-3881/aae77c
  • 语种:English
  • 出版社:American Institute of Physics
  • 摘要:Atmospheric retrievals on exoplanets usually involve computationally intensive Bayesian sampling methods. Large parameter spaces and increasingly complex atmospheric models create a computational bottleneck forcing a trade-off between statistical sampling accuracy and model complexity. It is especially true for upcoming JWST and ARIEL observations. We introduce ExoGAN, the Exoplanet Generative Adversarial Network, a new deep-learning algorithm able to recognize molecular features, atmospheric trace-gas abundances, and planetary parameters using unsupervised learning. Once trained, ExoGAN is widely applicable to a large number of instruments and planetary types. The ExoGAN retrievals constitute a significant speed improvement over traditional retrievals and can be used either as a final atmospheric analysis or provide prior constraints to subsequent retrieval.
  • 关键词:methods: statistical;planets and satellites: atmospheres;radiative transfer;techniques: spectroscopic
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