Decameter-sized Earth Impactors -- II: A Bayesian Inference Approach to Meteoroid Ablation Modeling
Ian Chow, Peter G. Brown

TL;DR
This paper introduces a Bayesian inference method to analyze fireball light curves, revealing three distinct structural groups among decameter-sized Earth impactors and their fragmentation behaviors.
Contribution
A novel Bayesian inference approach for estimating physical properties of meteoroids from fireball data, enabling detailed population-level analysis of impactor structures.
Findings
Identified three structural groups of impactors: weak, heterogeneous, and strong.
Discovered two-phase fragmentation process at specific pressure thresholds.
Most decameter impactors lose mass primarily in the second fragmentation phase.
Abstract
Small asteroids and large meteoroids frequently impact the Earth, though their physical and material properties remain poorly understood. When observed as fireballs in Earth's atmosphere, these properties can be inferred from their ablation and fragmentation behavior. The 2022 release of previously classified United States Government (USG) satellite sensor data has provided hundreds of new fireball light curves, allowing for more detailed analysis. Here we present a new Bayesian inference method based on dynamic nested sampling that can robustly estimate these objects' physical parameters from their observed light curves, starting from relatively uninformative, flat priors. We validate our method against seven USG sensor-observed fireballs with independent ground-based observations and demonstrate that our results are consistent with previous estimates. We then apply our technique to…
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Taxonomy
TopicsAstro and Planetary Science · Planetary Science and Exploration · Gamma-ray bursts and supernovae
