Murrili meteorite's fall and recovery from Kati Thanda

On the 27th of November 2015, at 10:43:45.526 UTC, a fireball was observed across South Australia by ten Desert Fireball Network observatories lasting 6.1 s. A $\sim37$ kg meteoroid entered the atmosphere with a speed of 13.68\pm0.09\,\mbox{km s}^{-1} and was observed ablating from a height of 85 km down to 18 km, having slowed to 3.28\pm0.21 \,\mbox{km s}^{-1}. Despite the relatively steep 68.5$^\circ$ trajectory, strong atmospheric winds significantly influenced the darkfight phase and the predicted fall line, but the analysis put the fall site in the centre of Kati Thanda - Lake Eyre South. Kati Thanda has metres-deep mud under its salt-encrusted surface. Reconnaissance of the area where the meteorite landed from a low flying aircraft revealed a 60 cm circular feature in the muddy lake, less than 50 m from the predicted fall line. After a short search, which again employed light aircraft, the meteorite was recovered on the 31st December 2015 from a depth of 42 cm. Murrili is the first recovered observed fall by the digital Desert Fireball Network (DFN). In addition to its scientific value, connecting composition to solar system context via orbital data, the recover demonstrates and validates the capabilities of the DFN, with its next generation remote observatories and automated data reduction pipeline

Determining Fireball Fates Using the α-β Criterion

As fireball networks grow, the number of events observed becomes unfeasible to manage by manual efforts. Reducing and analyzing big data requires automated data pipelines. Triangulation of a fireball trajectory can swiftly provide information on positions and, with timing information, velocities. However, extending this pipeline to determine the terminal mass estimate of a meteoroid is a complex next step. Established methods typically require assumptions to be made of the physical meteoroid characteristics (such as shape and bulk density). To determine which meteoroids may have survived entry there are empirical criteria that use a fireball's final height and velocity - low and slow final parameters are likely the best candidates. We review the more elegant approach of the dimensionless coefficient method. Two parameters, α (ballistic coefficient) and β (mass loss), can be calculated for any event with some degree of deceleration, given only velocity and height information. α and β can be used to analytically describe a trajectory with the advantage that they are not mere fitting coefficients; they also represent the physical meteoroid properties. This approach can be applied to any fireball network as an initial identification of key events and determine on which to concentrate resources for more in-depth analyses. We used a set of 278 events observed by the Desert Fireball Network to show how visualization in an α-β diagram can quickly identify which fireballs are likely meteorite candidates. © 2019. The American Astronomical Society. All rights reserved

Recreating the OSIRIS-REx Slingshot Manoeuvre from a Network of Ground-Based Sensors

Optical tracking systems typically trade-off between astrometric precision and field-of-view. In this work, we showcase a networked approach to optical tracking using very wide field-of-view imagers that have relatively low astrometric precision on the scheduled OSIRIS-REx slingshot manoeuvre around Earth on September 22nd, 2017. As part of a trajectory designed to get OSIRIS-REx to NEO 101955 Bennu, this flyby event was viewed from 13 remote sensors spread across Australia and New Zealand to promote triangulatable observations. Each observatory in this portable network was constructed to be as lightweight and portable as possible, with hardware based off the successful design of the Desert Fireball Network. Over a 4 hour collection window, we gathered 15,439 images of the night sky in the predicted direction of the OSIRIS-REx spacecraft. Using a specially developed streak detection and orbit determination data pipeline, we detected 2,090 line-of-sight observations. Our fitted orbit was determined to be within about 10~km of orbital telemetry along the observed 109,262~km length of OSIRIS-REx trajectory, and thus demonstrating the impressive capability of a networked approach to SSA

A Global Fireball Observatory

The world's meteorite collections contain a very rich picture of what the early Solar System would have been made of, however the lack of spatial context with respect to their parent population for these samples is an issue. The asteroid population is equally as rich in surface mineralogies, and mapping these two populations (meteorites and asteroids) together is a major challenge for planetary science. Directly probing asteroids achieves this at a high cost. Observing meteorite falls and calculating their pre-atmospheric orbit on the other hand, is a cheaper way to approach the problem. The Global Fireball Observatory (GFO) collaboration was established in 2017 and brings together multiple institutions (from Australia, USA, Canada, Morocco, Saudi Arabia, the UK, and Argentina) to maximise the area for fireball observation time and therefore meteorite recoveries. The members have a choice to operate independently, but they can also choose to work in a fully collaborative manner with other GFO partners. This efficient approach leverages the experience gained from the Desert Fireball Network (DFN) pathfinder project in Australia. The state-of-the art technology (DFN camera systems and data reduction) and experience of the support teams is shared between all partners, freeing up time for science investigations and meteorite searching. With all networks combined together, the GFO collaboration already covers 0.6% of the Earth's surface for meteorite recovery as of mid-2019, and aims to reach 2% in the early 2020s. We estimate that after 5 years of operation, the GFO will have observed a fireball from virtually every meteorite type. This combined effort will bring new, fresh, extra-terrestrial material to the labs, yielding new insights about the formation of the Solar System.Comment: Accepted in PSS. 19 pages, 9 figure

Determining Fireball Fates Using the α-β Criterion

As fireball networks grow, the number of events observed becomes unfeasible to manage by manual efforts. Reducing and analyzing big data requires automated data pipelines. Triangulation of a fireball trajectory can swiftly provide information on positions and, with timing information, velocities. However, extending this pipeline to determine the terminal mass estimate of a meteoroid is a complex next step. Established methods typically require assumptions to be made of the physical meteoroid characteristics (such as shape and bulk density). To determine which meteoroids may have survived entry there are empirical criteria that use a fireball's final height and velocity - low and slow final parameters are likely the best candidates. We review the more elegant approach of the dimensionless coefficient method. Two parameters, α (ballistic coefficient) and β (mass loss), can be calculated for any event with some degree of deceleration, given only velocity and height information. α and β can be used to analytically describe a trajectory with the advantage that they are not mere fitting coefficients; they also represent the physical meteoroid properties. This approach can be applied to any fireball network as an initial identification of key events and determine on which to concentrate resources for more in-depth analyses. We used a set of 278 events observed by the Desert Fireball Network to show how visualization in an α-β diagram can quickly identify which fireballs are likely meteorite candidates

Fireball streak detection with minimal CPU processing requirements for the Desert Fireball Network data processing pipeline

The detection of fireballs streaks in astronomical imagery can be carried out by a variety of methods. The Desert Fireball Network uses a network of cameras to track and triangulate incoming fireballs to recover meteorites with orbits and to build a fireball orbital dataset. Fireball detection is done on-board camera, but due to the design constraints imposed by remote deployment, the cameras are limited in processing power and time. We describe the processing software used for fireball detection under these constrained circumstances. Two different approaches were compared: (1) A single-layer neural network with 10 hidden units that were trained using manually selected fireballs and (2) a more traditional computational approach based on cascading steps of increasing complexity, whereby computationally simple filters are used to discard uninteresting portions of the images, allowing for more computationally expensive analysis of the remainder. Both approaches allowed a full night's worth of data (over a thousand 36-megapixel images) to be processed each day using a low-power single-board computer. We distinguish between large (likely meteorite-dropping) fireballs and smaller fainter ones (typical 'shooting stars'). Traditional processing and neural network algorithms both performed well on large fireballs within an approximately 30 000-image dataset, with a true positive detection rate of 96% and 100%, respectively, but the neural network was significantly more successful at smaller fireballs, with rates of 67% and 82%, respectively. However, this improved success came at a cost of significantly more false positives for the neural network results, and additionally the neural network does not produce precise fireball coordinates within an image (as it classifies). Simple consideration of the network geometry indicates that overall detection rate for triangulated large fireballs is calculated to be better than 99.7% and 99.9%, by ensuring that there are multiple double-station opportunities to detect any one fireball. As such, both algorithms are considered sufficient for meteor-dropping fireball event detection, with some consideration of the acceptable number of false positives compared to sensitivity