Abstract


In planetary defense, long period comets have remained a class by themselves. They are recognized as the potentially most devastating threat (i.e. “extinction level events”). But any new comet discovered on an impact trajectory would likely only be discovered as it passed Jupiter, just a few years before impact. However, with machine learning, meteor showers may offer a clue. The proposal: replace the data analyst in the ‘CAMS’ meteor shower survey program by deep learning algorithms and thus enable a global expansion and temporal coverage of a camera network that can detect the dust trails of those potentially hazardous long period comets that came close to Earth’s orbit in the past ten millennia.

The goal is to add years of extra warning time by providing comet searchers directions on where to look for comets when they are still far out. This task is particularly suited to a machine learning approach because of the large scale of data, the need for integration of surveys from around the globe without human intervention, and the need to operate for a long period of time.

The deep learning algorithms would be used to recognize meteors amongst false positives (e.g., satellites), and can triangulate the meteor trajectory in Earth’s atmosphere, its entry speed, and the pre-impact orbit in space through combining different camera perspectives to the same meteor.

Researchers

Andres Plata Stapper*
Antonio Ordonez*
Jack Collison*
Marcello di Cicco*
Susana Zoghbi*

* = Equal contribution

Mentors

Published at

@inproceedings{CiccoIMC17,
author = {Marcello De Cicco and S. Zoghbi and A. P. Stapper and A. J. Ordonez and J. Collison and P. S. Gural and S. Ganju and J.-L. Galache and P. Jenniskens},
title = {Artificial Intelligence Techniques applied to Automating Meteor Validation and Trajectory Quality Control to Direct the Search for Long Period Comets},
booktitle = {IMC},
year = {2017}
}
[International Meteor Conference 2017] [Marcello's presentation]


@inproceedings{ZoghbiGTC17,
author = {S. Zoghbi and M. Cicco and A. P. Stapper and A. J. Ordonez and J. Collison and P. S. Gural and S. Ganju and J.-L. Galache and P. Jenniskens},
title = {Artificial Intelligence Techniques applied to Automating Meteor Validation and Trajectory Quality Control to Direct the Search for Long Period Comets},
booktitle = {IMC},
year = {2017}
}
[GTC Munich 2017] [Susana's presentation]