| Issue |
A&A
Volume 634, February 2020
|
|
|---|---|---|
| Article Number | A45 | |
| Number of page(s) | 6 | |
| Section | Numerical methods and codes | |
| DOI | https://doi.org/10.1051/0004-6361/201935983 | |
| Published online | 04 February 2020 | |
Identifying Earth-impacting asteroids using an artificial neural network
1
Sterrewacht, Leiden University, Leiden, The Netherlands
e-mail: jdavidhefele@gmail.com
2
LIACS, Leiden University, Leiden, The Netherlands
Received:
29
May
2019
Accepted:
9
December
2019
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.
Key words: comets: general / minor planets / asteroids: general / methods: data analysis / methods: statistical
© ESO 2020
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