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  • 标题:Identifying Earth-impacting asteroids using an artificial neural network
  • 本地全文:下载
  • 作者:John D. Hefele ; Francesco Bortolussi ; Simon Portegies Zwart
  • 期刊名称:Astronomy & Astrophysics
  • 印刷版ISSN:0004-6361
  • 电子版ISSN:1432-0746
  • 出版年度:2020
  • 卷号:634
  • 页码:1-6
  • DOI:10.1051/0004-6361/201935983
  • 语种:English
  • 出版社:EDP Sciences
  • 摘要:By means of a fully connected artificial neural network, we identified asteroids with the potential to impact Earth. The resulting instrument, named the Hazardous Object Identifier (HOI), was trained on the basis of an artificial set of known impactors which were generated by launching objects from Earth’s surface and integrating them backward in time. HOI was able to identify 95.25% of the known impactors simulated that were present in the test set as potential impactors. In addition, HOI was able to identify 90.99% of the potentially hazardous objects identified by NASA, without being trained on them directly.
  • 关键词:encomets: generalminor planetsasteroids: generalmethods: data analysismethods: statistical
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