Datasets Comparison

This folder contains all results, validation tests, and sensitivity analyses for the paper "Inferring Meteoroid Properties with Dynamic Nested Sampling: A Case Study of Orionid and Capricornid Shower Meteors", plus the conda environment used to reproduce the simulations (cluster strongly recommended). - Results : contains the main CAMO+EMCCD and EMCCD-only solutions. - Validation : contains 8 test cases (4 CAMO+EMCCD and 4 EMCCD-only). - Sensitivity_Analysis : contains runs testing sensitivity to luminous efficiency assumptions and grain density variations.

Install code (same conda env): - https://github.com/wmpg/WesternMeteorPyLib - https://dynesty.readthedocs.io/en/v3.0.0/ - https://github.com/MaximilianVovk/WMPG-repoMAX To reproduce run this code in a cluster with a lot of nodes or the solutions will take more than days to be generated: WMPG-repoMAX/Code/DynNestSampl/DynNestSapl_metsim.py The code implements Dynamic Nested Sampling to define uncertainty estimate for the meteor base on lag and luminosity data. It reads automatically EMCCD and CAMO .pickle data but it can also work with MetSim json data if path and file name are in the input directory. You can process specific files or folders by separating them via a ',' comma. More detail are in the word document WMPG-repoMAX/Code/DynNestSampl/how_to_run-DynNestSapl_metsim.docx Usage: python "WMPG-repoMAX\Code\DynNestSampl\DynNestSapl_metsim.py" "C:\Users\maxiv\Documents\INPUT-FOLDER" --output_dir "C:\Users\maxiv\Desktop\OUTPUT-FOLDER" --prior "C:\Users\maxiv\WMPG-repoMAX\Code\DynNestSampl\stony_meteoroid.prior"

Statistics, Meteor, Meteoroid

Creative Commons Attribution 4.0 International

This folder contains all results, validation tests, and sensitivity analyses for the paper "Inferring Meteoroid Properties with Dynamic Nested Sampling: A Case Study of Orionid and Capricornid Shower Meteors", plus the conda environment used to reproduce the simulations (cluster strongly recommended). - Results : contains the main CAMO+EMCCD and EMCCD-only solutions. - Validation : contains 8 test cases (4 CAMO+EMCCD and 4 EMCCD-only). - Sensitivity_Analysis : contains runs testing sensitivity to luminous efficiency assumptions and grain density variations.

Install code (same conda env): - https://github.com/wmpg/WesternMeteorPyLib - https://dynesty.readthedocs.io/en/v3.0.0/ To reproduce run this code in a cluster with a lot of nodes or the solutions will take more than days to be generated: https://github.com/wmpg/WesternMeteorPyLib/blob/master/wmpl/Dynesty/DynestyMetSim.py The code implements Dynamic Nested Sampling to define uncertainty estimate for the meteor base on lag and luminosity data. It reads automatically EMCCD and CAMO .pickle data but it can also work with MetSim json data if path and file name are in the input directory. You can process specific files or folders by separating them via a ',' comma. More detail are in the README.md in https://github.com/wmpg/WesternMeteorPyLib/blob/master/wmpl/Dynesty Usage: python "wmpl/Dynesty/DynestyMetSim.py" "C:\Users\maxiv\Documents\INPUT-FOLDER" --output_dir "C:\Users\maxiv\Desktop\OUTPUT-FOLDER" --prior "C:\Users\maxiv\WMPG-repoMAX\Code\DynNestSampl\stony_meteoroid.prior" 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.

Statistics, Meteor, Meteoroid

Creative Commons Attribution 4.0 International