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Researcher profile

Hadrien A. R. Devillepoix

Hadrien A. R. Devillepoix contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
astro-ph.EPastro-ph.IMphysics.geo-phMachine Learning

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Published work

7 published item(s)

preprint2026arXiv

A Cloud-Based Tool for Meteorite Recovery Using Drones and Machine Learning

We present a cloud-based tool that uses drones and machine learning to help recover instrumentally observed meteorite falls. We showcase a collection of improvements made upon previous iterations of our system, as well as detail the successes and limitations of this technique when applied to observed meteorite falls in South and Western Australia. This tool is available to the meteoritics research community upon request at https://find.gfo.rocks.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Arpu Kuilpu: An H5 from the Outer Main Belt

On 1 June 2019, just before 7:30 PM local time, the Desert Fireball Network detected a -9.3 magnitude fireball over South Australia near the Western Australia border. The event was observed by six fireball observatories, and lasted for five seconds. One station was nearly directly underneath the trajectory, greatly constraining the trajectory solution. This trajectory's backward numerical integrations indicate that the object originated from the outer main belt with a semi-major axis of 2.75 au. A light curve was also extracted and showed that the body experienced very little fragmentation during its atmospheric passage. A search campaign was conducted with several Desert Fireball Network team members and other volunteers. One 42 g fragment was recovered within the predicted fall area based on the dark flight model. Based on measurements of short-lived radionuclides, the fragment was confirmed to be a fresh fall. The meteorite, Arpu Kuilpu, has been classified as an H5 ordinary chondrite. This marks the fifth fall recovered in Australia by the Desert Fireball Network, and the smallest meteoroid ($\simeq 2$ kg) to ever survive entry and be recovered as a meteorite.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Minimoon still on the loose

On Aug 22, 2016, a bright fireball was observed by the Desert Fireball Network in South Australia. Its pre-atmosphere orbit suggests it was temporarily captured by the Earth-Moon system before impact. A search was conducted two years after the fall, and a meteorite was found after 6 days of searching. The meteorite appeared relatively fresh, had a mass consistent with fireball observation predictions, and was at the predicted location within uncertainties. However, the meteorite did show some weathering and lacked short-lived radionuclides ($^{58}$Co, $^{54}$Mn). A terrestrial age based on cosmogenic $^{14}$C dating was determined; the meteorite has been on the Earth&#39;s surface for $3.2\pm1.3$ kyr, ruling out it being connected to the 2016 fireball. Using an upper limit on the pleistieocene terrain age and the total searched area, we find that the contamination probability from another fall is $<2\%$. Thus, the retrieval of the &#34;wrong&#34; meteorite is at odds with the contamination statistics. This is a key example to show that fireball-meteorite pairings should be carefully verified.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package

The Astropy Project supports and fosters the development of open-source and openly-developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package $\texttt{astropy}$, which serves as the foundation for more specialized projects and packages. In this article, we summarize key features in the core package as of the recent major release, version 5.0, and provide major updates for the Project. We then discuss supporting a broader ecosystem of interoperable packages, including connections with several astronomical observatories and missions. We also revisit the future outlook of the Astropy Project and the current status of Learn Astropy. We conclude by raising and discussing the current and future challenges facing the Project.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Trajectory, recovery, and orbital history of the Madura Cave meteorite

On the 19th June 2020 at 20:05:07 UTC, a fireball lasting 5.5 s was observed above Western Australia by three Desert Fireball Network observatories. The meteoroid entered the atmosphere with a speed of $14.00 \pm 0.17$ km s$^{-1}$ and followed a $58^{\circ}$ slope trajectory from a height of 75 km down to 18.6 km. Despite the poor angle of triangulated planes between observatories (29$^{\circ}$) and the large distance from the observatories, a well constrained kilo-size main mass was predicted to have fallen just South of Madura in Western Australia. However, the search area was predicted to be large due to the trajectory uncertainties. Fortunately, the rock was rapidly recovered along the access track during a reconnaissance trip. The 1.072 kg meteorite called Madura Cave was classified as an L5 ordinary chondrite. The calculated orbit is of Aten type (mostly contained within the Earth&#39;s orbit), the second time only a meteorite is observed on such an orbit after Bunburra Rockhole. Dynamical modelling shows that Madura Cave has been in near-Earth space for a very long time. The NEO dynamical lifetime for the progenitor meteoroid is predicted to be $\sim87$ Myr. This peculiar orbit also points to a delivery from the main asteroid belt via the $\nu6$ resonance, and therefore an origin in the inner belt. This result contributes to drawing a picture for the existence of a present-day L chondrite parent body in the inner belt.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

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 analysing 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&#39;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 visualisation in an $α$ - $β$ diagram can quickly identify which fireballs are likely meteorite candidates.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

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--DFN--uses a network of cameras to track and triangulate incoming fireballs to recover meteorites with orbits. Fireball detection is done on-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. A cascading approach was implemented, whereby computationally simple filters are used to discard uninteresting portions of the images, allowing for more computationally expensive analysis of the remainder. This allows a full night&#39;s worth of data; over 1000 36 megapixel images to be processed each day using a low power single board computer. The algorithms chosen give a single camera successful detection large fireball rate of better than 96 percent, when compared to manual inspection, although significant numbers of false positives are generated. The overall network detection rate for triangulated large fireballs is estimated to be better than 99.8 percent, by ensuring that there are multiple double stations chances to detect one fireball.

0 citations0 reviewsNo code linkedOpen access