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Michael A. Frazer

Publications and source records attributed to Michael A. Frazer.

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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.

astro-ph.EP

Simulated LSST Observations of Real Metre-scale Imminent Impactors

As of mid-2026, 11 objects have been discovered prior to impacting the Earth, with warning times between 2 - 20 hours. Using real metre-sized Earth impactors from the last decade, we ask the question: ``If the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) had been operating over the last decade, how many imminent impactors would it have observed and discovered pre-impact, and how early would these discoveries have been?'' We use the LSST Solar System Survey Simulator Sorcha and a population of real fireballs observed by orbital sensors over the last decade to investigate which events would have been observed pre-impact. We find that the LSST would have observed 30 (13.9%) of the 216 simulated objects, with most objects receiving 2 - 4 observations. Using the default linking algorithm, only two (0.9%) of these objects would have been `discovered' pre-impact. Using a modified linking algorithm better suited to fast moving objects, this increases to eight (3.7%). Based on this, we predict that the LSST will discover 8 +/- 2 imminent impactors over its nominal 10 year survey, at the low end of previous estimations. However, we predict these objects to be discovered 4 days pre-impact, substantially earlier than the current average. This will bring significant opportunities for telescopic follow-up, targeted fireball observations, planetary defence planning, and public engagement. There is also significant potential for precovery for impactors observed by the LSST but discovered by other surveys, instantly lengthening observation arcs and thereby reducing the orbital and impact location uncertainties. In some cases, these observations may also enable the linkage of telescopic observations with observed fireballs post-impact, providing valuable pre-impact astrometric and photometric data. This has significant implications for both asteroid research and planetary defence.

astro-ph.EP

Producing High-Resolution Martian Surface Temperature Maps Using VIR-TIR Relationships

Thermal infrared data (TIR; 8 - 15 $μm$) has a wide range of applications in Earth and planetary remote sensing. On Mars, this includes deriving thermal inertia (TI), which describes surface physical characteristics (e.g. particle size, degree of cementation) and is key for understanding geologic processes, assessing in-situ resource utilisation (ISRU) environments, and assisting mission planning. However, TI data from the THEMIS instrument is limited to 100 m/pixel resolution. Hyperspectral visible and near-infrared data (VIR; 0.5 - 5 $μm$) compliments TIR data by providing information on surface composition and is provided by the CRISM instrument at 12 m/pixel. In this work, we generate a machine learning regressor-based model to constrain relationships between THEMIS TI and CRISM VIR images at THEMIS resolution, and predict TI values from CRISM spectra with high accuracy (R2 $\sim$ 0.90, RMSE $\sim$ 23.6 TIU). We use the model to produce a downscaled TI map at a spatial resolution of 12 m/pixel, an order of magnitude finer than currently available, revealing decametre-scale features previously unresolved in THEMIS data.

astro-ph.EP