Consistency between dynamical modeling and photometrically derived masses of fireballs

TL;DR

This paper introduces a new inverse modeling method to reconstruct meteoroid deceleration and mass-loss histories from sparse observational data, improving consistency with physical properties.

Contribution

The method combines analytical formalism with numerical optimization to derive physically consistent solutions from limited fireball observations, expanding analysis capabilities.

Findings

Achieves 88% convergence with height-velocity data alone.

Produces density estimates consistent with PE classes for over half of solutions.

Reveals a continuous bulk-density distribution, contrasting with fixed PE-based categories.

Abstract

We present a three-point inverse solution for reconstructing meteoroid deceleration and mass-loss histories from sparse observations constrained only by the entry, peak-brightness, and terminal points. The method combines the $\alpha$-$\beta$ analytical formalism with a derivative-free global optimizer and a numerical inversion of the height-velocity relation, enabling the retrieval of physically consistent solutions even when full velocity profiles are unavailable. Applied to the 2017-2018 European Fireball Network (EN) catalog, the approach achieves an 88% convergence rate when fitting only height-velocity pairs, and 63% when terminal and initial masses are also imposed. 52% of mass-constrained solutions (34% overall) yield bulk densities consistent with their $PE$ classes, with higher strength emerging as the primary discriminator among events retaining coherent classifications when only 3 points are used as input data. Rapidly evolving high-energy, high-mass events show the largest incompatibility with the $\alpha$-$\beta$ model. The inversion produces a continuous bulk-density distribution spanning $\sim$300-4000 kg$\,$m$^{-3}$, in contrast to the discrete densities fixed by $PE$-based categories. The EN fireball dataset is now supplemented with self-consistent $\alpha$ and $\beta$ estimates.