Inferring Meteoroid Properties with Dynamic Nested Sampling: A Case Study of Orionid and Capricornid Shower Meteors

TL;DR

This paper introduces a robust Bayesian method using Dynamic Nested Sampling to accurately estimate meteoroid properties from optical meteor data, improving physical characterization and uncertainty quantification.

Contribution

The study develops a novel, automated statistical framework for inferring meteoroid densities and fragmentation scenarios from meteor light curves and deceleration data.

Findings

Recovered known parameters in synthetic tests.

Median bulk densities: Orionids ~159 kg/m³, Capricornids ~333 kg/m³.

Orionids are low-density cometary; Capricornids show higher densities.

Abstract

Accurate estimation of meteoroid bulk density is crucial for assessing spacecraft impact hazards from sub-millimeter to millimeter-sized meteoroids. Previous studies often used manual tuning or optimization methods to fit ablation and fragmentation models to optical meteor data, but subjective choices made physical properties and uncertainties difficult to compare. We develop a global, statistically robust method that uses Dynamic Nested Sampling to fit an erosion-fragmentation model to meteor light curves and deceleration measured by the Canadian Automated Meteor Observatory (CAMO) mirror tracking system and Electron-Multiplied CCD (EMCCD) cameras. Applied to 15 shower meteors, the method returns posterior distributions and Bayesian evidences for single- and double-fragmentation scenarios. Tests on four synthetic cases recover the known inputs, with best-guess solutions matching the true parameters. For 9 Orionids and 6 Alpha Capricornids with masses 1e-6 to 1e-5 kg, the median bulk density is 159 (+558/-57) kg/m3 for Orionids and 333 (+1089/-114) kg/m3 for Alpha Capricornids. Orionids are consistent with low-density cometary material, while Alpha Capricornids are systematically denser and show a second density cluster near 1300 kg/m3, consistent with higher-density asteroidal material. This framework enables automated, statistically rigorous characterization of meteoroid properties and will be extended to larger samples of shower and sporadic meteors across orbital classes.