2022
Stevenson, Emma; Martinez, Riansares; Rodriguez-Fernandez, Victor; Camacho, David
Predicting the effects of kinetic impactors on asteroid deflection using end-to-end deep learning Proceedings Article
In: 2022 IEEE Congress on Evolutionary Computation (CEC), pp. 1-8, Padua, Italy, 2022.
Abstract | Links | BibTeX | Tags: Asteroid Deflection, Deep Learning, Planetary Defence, Self-supervised Learning, Time Series, Transformers
@inproceedings{9870215,
title = {Predicting the effects of kinetic impactors on asteroid deflection using end-to-end deep learning},
author = {Emma Stevenson and Riansares Martinez and Victor Rodriguez-Fernandez and David Camacho},
doi = {10.1109/CEC55065.2022.9870215},
year = {2022},
date = {2022-07-18},
urldate = {2022-07-18},
booktitle = {2022 IEEE Congress on Evolutionary Computation (CEC)},
pages = {1-8},
address = {Padua, Italy},
abstract = {One possible approach to deflect the trajectory of an asteroid on a collision course with the Earth, and prevent a potentially devastating impact, is the use of a kinetic impactor. The upcoming NASA DART and ESA Hera space missions will be the first to study and demonstrate this technique, by driving a spacecraft into the moon of a binary asteroid system with the aim of altering its momentum, and knocking it off course. In this work, we seek to predict critical parameters associated with such an impact, namely the momentum transfer efficiency and axial ratio of the target body, based on light curve data observed from ground before and after the impact in order to give insights into the real effect of the deflection effort. We present here our approach to this problem, which we address from a purely data-driven perspective based on simulated data provided as a part of the Andrea Milani Planetary Defence Challenge, organised by the EU H2020 Stardust-R research network in conjunction with ESA. Formulating the problem as a time series regression task, we develop an end-to-end deep learning pipeline in which we apply the latest advances in deep learning for time series, such as the use of the Transformer architecture as well as ensembling and self-supervised learning techniques. Exploiting these techniques for the challenge, we achieved second place out of the student teams, and fifth place overall without relying on any a priori knowledge of the physics of the asteroid system.},
keywords = {Asteroid Deflection, Deep Learning, Planetary Defence, Self-supervised Learning, Time Series, Transformers},
pubstate = {published},
tppubtype = {inproceedings}
}
One possible approach to deflect the trajectory of an asteroid on a collision course with the Earth, and prevent a potentially devastating impact, is the use of a kinetic impactor. The upcoming NASA DART and ESA Hera space missions will be the first to study and demonstrate this technique, by driving a spacecraft into the moon of a binary asteroid system with the aim of altering its momentum, and knocking it off course. In this work, we seek to predict critical parameters associated with such an impact, namely the momentum transfer efficiency and axial ratio of the target body, based on light curve data observed from ground before and after the impact in order to give insights into the real effect of the deflection effort. We present here our approach to this problem, which we address from a purely data-driven perspective based on simulated data provided as a part of the Andrea Milani Planetary Defence Challenge, organised by the EU H2020 Stardust-R research network in conjunction with ESA. Formulating the problem as a time series regression task, we develop an end-to-end deep learning pipeline in which we apply the latest advances in deep learning for time series, such as the use of the Transformer architecture as well as ensembling and self-supervised learning techniques. Exploiting these techniques for the challenge, we achieved second place out of the student teams, and fifth place overall without relying on any a priori knowledge of the physics of the asteroid system.