Characterizing the Population of Small & Slow Meteoroids: New Physical Characterization Method for Satellite Risk Assessment

Meteoroid impacts pose a critical threat to spacecraft. Natural objects as small as sub-millimeter ( >0.2 mm) upon impact, can deliver enough kinetic energy to damage or disable satellites[1]. Impact damage is governed by the meteoroid’s velocity, mass and bulk density as given by the ballistic limit equations [2][3]. Slow sporadic meteoroids (velocity < 20 km/s) dominate the meteoroid flux on Earth, but are very difficult to observe by both radar and optical methods, as they produce little ionization and light. Recent work shows that approximately 16% of these slow meteoroids are iron-rich [4], making them especially important to study as their higher bulk density will result in higher impact hazard for satellites in orbit.

In this work, we observe slow and small sporadic meteoroids using high-sensitivity Electron Multiplying CCD (EMCCD) video cameras. These EMCCDs, have a limiting meteor sensitivity of magnitude +8, 50 m/pixel spatial resolution at 100 km, and operate at 32 frames per second (FPS). To complement these measurements we fuse EMCCD data with high resolution imagery from the Canadian Automated Meteor Observatory’s (CAMO) mirror tracking system. CAMO achieves a spatial resolution of 6 m/pixel at 100 km and operates at 100 FPS with limiting detection sensitivity of +7. This provides high-cadence and precision observations of fragmentation and morphology. When merged with higher sensitivity EMCCD data (which captures the onset of ablation earlier) these measurements provide are critical constraints for modelling meteoroid structure.

Our study is based on the analysis of 100 slow sporadic meteors for which we have simultaneous CAMO and EMCCD data. As shown in Fig. 1 our data clearly shows two separate populations of meteoroids : (a) porous cometary particles that fragment and decelerate rapidly at high altitudes, and (b) dense, iron-rich or stony asteroidal meteoroids with minimal deceleration. These findings support past results [5].

Fig. 1. 100 slow meteoroids jointly recorded by EMCCD and CAMO video systems and their total trail length as a function of the energy required to be intercepted through atmospheric molecular collisions to begin erosive fragmentation and F-parameter. The F-parameter is a normalized measure of the location of the peak brightness along the trail with 0 indicating peak at the start and 1 peak brightness at the end of the trail. These two populations are denoted with (a) and (b). Following the work in [4] iron meteoroid candidates are those that have F < 0.31, Trail Length < 11 km and erosion Energy per unit cross section > 4 MJ/m.

To robustly infer the physical properties of these meteoroids based on our observations, we develop a novel method using Dynamic Nested Sampling [6]. This Bayesian inference technique, implemented via the dynesty Python package [7], is specifically designed to handle high-dimensional, degenerate, and multimodal parameter spaces. We use the Borovička et al. (2007) [8] meteoroid ablation and fragmentation to provide model fits to measured brightness and deceleration of meteors. This model  assumes meteoroids fragment by continuous ejection of micrometer-sized grains. In combination with Dynamic Nested Sampling, we are able to define statistically significant solutions with credible intervals (CIs) for all the meteoroid physical characteristics. We define a custom log-likelihood function that jointly incorporates measurements of both luminosity and meteoroid dynamics. Unlike traditional forward-modeling approaches [8][9], which are challenged to produce uncertainty estimation and solution degeneracy, our method allows rigorous quantification of posterior distributions, capturing model degeneracies, and assessment of the uniqueness of retrieved solutions.

Our work provides the first probabilistic framework to extract meteoroid mass, bulk density, and fragmentation properties from atmospheric observations of meteoroids with quantified uncertainties. These results offer valuable inputs for space environment models like NASA’s MEM [10] and ESA’s IMEM [11] helping safeguard satellites from an often overlooked impact threat.

References:

[1] Moorhead, A. V. et al. (2019). Planetary and Space Science, 165, 208–218.

[2] Christiansen, E. L. (2001). NASA TP-2001-210788.

[3] Moorhead, A. V. et al. (2020). NASA/TM–20205011017.

[4] Mills, T. M. et al. (2021). Monthly Notices of the Royal Astronomical Society, 506(4), 6012–6024.

[5] Vida, D., Brown, P. G., & Campbell-Brown, M. (2018). Monthly Notices of the Royal Astronomical Society, 479(4), 4307–4319.

[6] Higson, E., Handley, W., Hobson, M., & Lasenby, A. (2019). Statistics and Computing, 29, 891–913.

[7] Speagle, J. S. (2020). Monthly Notices of the Royal Astronomical Society, 493(3), 3132–3158.

[8] Borovička, J. et al. (2007). Astronomy & Astrophysics, 473(2), 661–672.

[9] Buccongello, N., Brown, P. G., Vida, D., & Pinhas, A. (2024). Icarus, 410, 115907.

[10] Moorhead, A. V. (2020) NASA/TM-2020-220555.

[11] Soja, R. H., et al. (2019) Astronomy & Astrophysics, 628 (2019): A109.