Inferring meteoroid properties with dynamic nested sampling: A case study of orionid and capricornid shower meteors

Importance: Accurate estimation of meteoroid bulk density is crucial for assessing spacecraft impact hazards of sub-millimeter to millimeter-sized meteoroids, which represent the bulk of the hazard. Research Gap: Previous studies utilized manual or optimization methods for fitting numerical meteoroid ablation and fragmentation models to optical meteor observations. However, these methods struggled with reliably estimating meteoroid physical properties and the associated uncertainties due to the subjectivity of the modeling approach. Objective: We aim to develop a global and statistically robust optimization method for inferring the physical properties of individual meteors, focusing on bulk density and fragmentation behavior, using multi-instrument optical data. Methodology: We apply Dynamic Nested Sampling to fit an erosion-fragmentation model to measurements of meteor light curve and deceleration. The method was applied to 15 shower meteors observed by the Canadian Automated Meteor Observatory’s (CAMO) mirror tracking and Electron-Multiplied Charge Coupled Device (EMCCD) systems. The method yields posterior distributions and Bayesian evidences for single and double fragmentations. Key Findings: Validation against four synthetic test cases demonstrated accurate recovery of known inputs, with best-guess solutions matching true parameters. We applied this method to 9 Orionids (ORI) and 6 α Capricornids (CAP) ranging in mass from 1 0 − 6 to 1 0 − 5 kg. The median bulk density was measured as 15 9 − 57 + 558 kg/m 3 for Orionid meteors and 33 3 − 114 + 1089 kg/m 3 for Capricornid meteors. These results are consistent with earlier studies: Orionids exhibit characteristics expected for meteoroids of cometary origin, whereas α Capricornids show systematically higher bulk densities. The CAP results show a second cluster around 1300 kg/m 3 , more inline with higher density asteroidal material, but our method achieves this using a more consistent and statistically robust estimation of uncertainties. Implications: The developed framework enables automated, statistically rigorous characterization of meteoroid physical properties. The method will be applied to more shower and sporadic meteors to characterize material properties of objects across orbital classes.