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Auriane Egal

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astro-ph.EP astro-ph.IM Machine Learning

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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.

preprint2020arXiv

A new method for measuring the meteor mass index: application to the 2018 Draconid meteor shower outburst

Context. Several authors predicted an outburst of the Draconid meteor shower in 2018, but with an uncertain level of activity. Aims. Optical meteor observations were used to derive the population and mass indices, flux, and radiant positions of Draconid meteors. Methods. 90 minutes of multi-station observations after the predicted peak of activity were performed using highly sensitive Electron Multiplying Charge Coupled Device (EMCCD) cameras. The data calibration is discussed in detail. A novel maximum likelihood estimation method of computing the population and mass index with robust error estimation was developed. We apply the method to observed Draconids and use the values to derive the flux. Meteor trajectories are computed and compared to predicted radiant positions from meteoroid ejection models. Results. We found that the mass index was $1.74 \pm 0.18$ in the 30 minute bin after the predicted peak, and $2.32 \pm 0.27$ in the next 60 minutes. The location and the dispersion of the radiant matches well to modeled values, but there is an offset of $0.4^{\circ}$ in solar longitude.