Bayesian Reconstruction of Meteors with the PAIP-V Multichannel Detector

This paper addresses the problem of reconstructing events recorded by orbital and ground-based detectors with low angular but high temporal resolution. We demonstrate that even in this scenario, high-precision spatiotemporal reconstruction is achievable by developing a unified algorithm that combines information on the event’s geometry, kinematics, and dynamics (light curve). This is particularly crucial when the photodetector contains numerous structural gaps between its channels, resulting in only a partial recording of the event. This work proposes a Bayesian method for reconstructing track-like events (such as meteor and satellite tracks), implemented using the PyMC library. The parametric model accounts for both the intrinsic features of the phenomenon and the specifics of the detection process, with the posterior parameter distribution being built via MCMC sampling. The method was tested on al sample of 2022 Geminid meteors recorded by the ground-based PAIP-V detector located in the Murmansk region.