paper-with-me

Papers

NEOviz: Uncertainty-Driven Visual Analysis of Asteroid Trajectories

2024-11-05 · Fangfei Lan, Malin Ejdbo, Joachim Moeyens, Bei Wang, Anders Ynnerman, Alexander Bock

We introduce NEOviz, an interactive visualization system designed to assist planetary defense experts in the visual analysis of the movements of near-Earth objects in the Solar System that might prove hazardous to Earth. Asteroids are often discovered using optical telescopes and their trajectories are calculated from images, resulting in an inherent asymmetric uncertainty in their position and velocity. Consequently, we typically cannot determine the exact trajectory of an asteroid, and an ensemble of trajectories must be generated to estimate an asteroid's movement over time. When propagating these ensembles over decades, it is challenging to visualize the varying paths and determine their potential impact on Earth, which could cause catastrophic damage. NEOviz equips experts with the necessary tools to effectively analyze the existing catalog of asteroid observations. In particular, we present a novel approach for visualizing the 3D uncertainty region through which an asteroid travels, while providing accurate spatial context in relation to system-critical infrastructure such as Earth, the Moon, and artificial satellites. Furthermore, we use NEOviz to visualize the divergence of asteroid trajectories, capturing high-variance events in an asteroid's orbital properties. For potential impactors, we combine the 3D visualization with an uncertainty-aware impact map to illustrate the potential risks to human populations. NEOviz was developed with continuous input from members of the planetary defense community through a participatory design process. It is exemplified in three real-world use cases and evaluated via expert feedback interviews.

📄 PDF Abstract BibTeX arXiv:2411.02812

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

ASTEROID: A Spatiotemporal Information Transformer for Forecasting Multi-Step Time Series of Molecular Dynamics

2026-06-16 · Kexin Wu, Luonan Chen, Renxiao Wang arxiv

Molecular dynamics (MD) simulation is computationally demanding, particularly for large-scale systems requiring long-term analysis. Accurate forecast of the outcomes of a MD simulation is not only an attractive scientifi…

Prediction of Apophis Asteroid Flyby Optimal Trajectories and Data Fusion of Earth-Apophis Mission Launch Windows using Deep Neural Networks

2021-04-11 · Manuel Ntumba, Saurabh Gore, Jean-Baptiste Awanyo

In recent years, understanding asteroids has shifted from light worlds to geological worlds by exploring modern spacecraft and advanced radar and telescopic surveys. However, flyby in 2029 will be an opportunity to condu…

NEARBY Platform for Detecting Asteroids in Astronomical Images Using Cloud-based Containerized Applications

2019-01-14 · V. Bacu, A. Sabou, T. Stefanut, D. Gorgan 외

The continuing monitoring and surveying of the nearby space to detect Near Earth Objects (NEOs) and Near Earth Asteroids (NEAs) are essential because of the threats that this kind of objects impose on the future of our p…

Towards Asteroid Detection in Microlensing Surveys with Deep Learning

2022-11-04 · Preeti Cowan, Ian A. Bond, Napoleon H. Reyes

Asteroids are an indelible part of most astronomical surveys though only a few surveys are dedicated to their detection. Over the years, high cadence microlensing surveys have amassed several terabytes of data while scan…

Deep Learning

Taxonomic analysis of asteroids with artificial neural networks

2023-11-18 · Nanping Luo, Xiaobin Wang, Shenghong Gu, Antti Penttilä 외

We study the surface composition of asteroids with visible and/or infrared spectroscopy. For example, asteroid taxonomy is based on the spectral features or multiple color indices in visible and near-infrared wavelengths…