The Deflector Selector: A Machine Learning Framework for Prioritizing Hazardous Object Deflection Technology Development

TL;DR

This paper introduces the Deflector Selector, a machine learning framework that predicts the most effective deflection technology for hazardous objects, aiding prioritization of development efforts based on object characteristics.

Contribution

The paper presents a novel machine learning model trained on simulation data to evaluate and prioritize deflection technologies for hazardous near-Earth objects.

Findings

Machine learning accurately predicts effective deflection methods.

Nuclear explosives, impactors, and gravity tractors tested on simulated populations.

Model helps prioritize technology development based on object parameters.

Abstract

Several technologies have been proposed for deflecting a hazardous Solar System object on a trajectory that would otherwise impact the Earth. The effectiveness of each technology depends on several characteristics of the given object, including its orbit and size. The distribution of these parameters in the likely population of Earth-impacting objects can thus determine which of the technologies are most likely to be useful in preventing a collision with the Earth. None of the proposed deflection technologies has been developed and fully tested in space. Developing every proposed technology is currently prohibitively expensive, so determining now which technologies are most likely to be effective would allow us to prioritize a subset of proposed deflection technologies for funding and development. We present a new model, the Deflector Selector, that takes as its input the characteristics of a hazardous object or population of such objects and predicts which technology would be able to perform a successful deflection. The model consists of a machine-learning algorithm trained on data produced by N-body integrations simulating the deflections. We describe the model and present the results of tests of the effectiveness of nuclear explosives, kinetic impactors, and gravity tractors on three simulated populations of hazardous objects.