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Just look UP
Presented by:
Eleanor Sansom
Eleanor Sansom
Curtin University
Martin Towner
Curtin University
Hadrien Devillepoix
Curtin University
Martin Cupak
Curtin University
Seamus Anderson
Curtin University
Phil Bland
Curtin University
Robert Howie
Curtin University
Ben Hartig
Curtin University
Patrick Shober
Curtin University
Trent Jansen-Sturgeon
STELaRLab, Lockheed Martin Australia
The Desert Fireball Network (DFN) in Australia was initiated in 2007 with 4 film cameras, and since 2015 has expanded to over 40 digital systems probing ~3 million km2 of sky every night. The DFN now forms part of the even larger Global Fireball Observatory (120 systems across 8 countries). Observing fireballs using such a distributed system allows atmospheric trajectories to be accurately re-created to calculate a meteoroid's orbit, and potential fall positions. This distributed facility also observes other phenomena, from rocket launches to satellite passes and re-entering spacecraft. The DFN team were heavily involved in the scientific observation campaign of JAXA’s Hayabusa-2 sample return capsule – a man-made fireball – operating infrasound, seismic, UHF and other optical sensors. Such studies allow us to characterise properties of hypersonic trajectories for space debris re-entry and asteroid hazard mitigation. Large, hazardous asteroid impacts are rare, and civilisation-ending events from km-sized aseroids are relatively well constrained. But, metre sized NEOs are still capable of posing a threat to people and property. The DFN orbital dataset is the largest of its kind, and is the best knowledge we have for the population of metre scale bodies in near Earth space. This will enable us to assess the risk to human assets on Earth and beyond, as well as informing future asteroid mining targets.
The DFN team has also recently developed next generation image recognition technology to locate meteorites on the ground. This detection of foreign or unusual objects has high potential for applications in domains such as search and rescue, and military operations. The machine learning algorithms applied can also be retrained for environmental or remote monitoring such as mine site rehabilitation, and solar panel health.
Category:
Invited plenary
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