A reproducible method to determine the meteoroid mass index
Petr Pokorny, Peter G. Brown

TL;DR
This paper introduces an automated Bayesian method to accurately determine meteoroid mass indices from radar and optical data, reducing uncertainties and improving reproducibility in meteoroid population studies.
Contribution
We developed a fully automated Bayesian approach using MultiNest to measure meteoroid mass indices and validate it on large datasets, addressing previous inconsistencies and uncertainties.
Findings
Best estimate for radar-based mass index: -2.10 ± 0.08
Optical data mass index: -2.08 ± 0.08
Mass index from Grun et al. 1985 is significantly larger
Abstract
Context. The determination of meteoroid mass indices is central to flux measurements and evolutionary studies of meteoroid populations. However, different authors use different approaches to fit observed data, making results difficult to reproduce and the resulting uncertainties difficult to justify. The real, physical, uncertainties are usually an order of magnitude higher than the reported values. Aims. We aim to develop a fully automated method that will measure meteoroid mass indices and associated uncertainty. We validate our method on large radar and optical datasets and compare results to obtain a best estimate of the true meteoroid mass index. Methods. Using MultiNest, a Bayesian inference tool that calculates the evidence and explores the parameter space, we search for the best fit of cumulative number vs. mass distributions in a four-dimensional space of variables…
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