A Statistical Approach to Quantifying Uncertainty in Meteoroid Physical Properties
Maximilian Vovk, Denis Vida, Peter G. Brown

TL;DR
This paper develops a statistical method to accurately estimate meteoroid bulk density and its uncertainties from optical meteor data, improving reliability over traditional approaches and aiding spacecraft safety assessments.
Contribution
It introduces an RMSD-based inversion method that provides robust density estimates and uncertainty bounds, overcoming limitations of observable-only selection techniques.
Findings
RMSD-based method yields more reliable density estimates.
Observable-only approaches can produce unphysical solutions.
Uncertainty bounds for meteoroid properties are now objectively derived.
Abstract
Meteoroid bulk density is a critical value required for assessing impact risks to spacecraft, informing shielding and mission design. Direct bulk density measurements for sub-millimeter to millimeter-sized meteoroids are difficult, often relying on forward modeling without robust uncertainty estimates. Methods based solely on select observables can overlook noise-induced biases and non-linear relations between physical parameters. This study aims to automate the inversion of meteoroid physical parameters from optical meteor data, focusing on bulk density and its associated uncertainties. We compare an observables-based selection method (PCA) with an RMSD-based approach used to select among millions of ablation model runs using full light and deceleration curves as constraints. After validating both approaches on six synthetic test cases, we apply them to two Perseid meteors recorded by…
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