Physical Parameters of Iron Meteoroids
Description
This dataset provides posterior distribution of the physical characteristics of 15 iron-rich meteoroids inferred via dynamic nested sampling from meteor observations. All the simulations runs are organized in 3 different folders based on the three luminous-efficiency (Tau) assumptions used (3%, 0.3%, 0.08%). For events with CAMO narrow-field tracking, wake plots (observed vs. best-guess lag/deceleration) and available spectra are included. Preferred solutions are in the Results folder and they satisfy both an observational fit criterion and a melting-temperature admissibility test (two events likely require an intermediate luminous efficiency).
Files
Steps to reproduce
Install code (same conda env): - https://github.com/wmpg/WesternMeteorPyLib - https://dynesty.readthedocs.io/en/v3.0.0/ The results in this dataset were generated using a modified erosion–fragmentation meteoroid entry model implemented in WesternMeteorPyLib (WMPL) and coupled to dynesty an implementation of dynamic nested sampling. Simulations can be re-run using the provided pickle, prior, and report files with the following script: https://github.com/wmpg/WesternMeteorPyLib/blob/master/wmpl/Dynesty/DynestyMetSim.py Plotting functions can be executed using the generated .dynesty output files; however, these files can be sensitive to system configuration and may not be portable across different operating systems or Conda environments. For this reason, all key posterior summaries and figures are included directly in the dataset. A Conda environment .yml file is provided to allow reconstruction of the software environment used for the simulations.
Institutions
- Western UniversityON, London