KDE Meteor Shower False Positive Estimation

This Python script is designed to perform advanced statistical analysis for estimating meteor shower false positives within a dataset, as described in our research paper. The script integrates several Python libraries and custom functions to handle data preprocessing, Kernel Density Estimation (KDE), calculation of various dissimilarity criteria, and statistical characterization of false positives.

Key Features:

1. Data Loading and Preprocessing:

   • Utilizes Pandas for loading and processing CSV files containing meteor observation data.
   • Extracts necessary columns and normalizes the data using z-score normalization, crucial for effective KDE application.

2. Kernel Density Estimation (KDE):

   • Employs Scikit-learn's KDE functionality with a Gaussian kernel to estimate the probability density function of the sporadic meteor background.
   • Supports bandwidth optimization through cross-validation and grid search, ensuring the best fit for the data.

3. Calculation of Dissimilarity Criteria:

   • Implements functions to calculate various D-criteria (e.g., Southworth-Hawkins, Drummond, Jopek, and Valsecchi) crucial for analyzing meteoroid orbits' similarity.

4. Sampling and False Positive Estimation:

   • Draws synthetic samples from the KDE-generated sporadic background and calculates the rate of false positives using the specified D-criteria.
   • Includes a rejection sampling mechanism to ensure samples adhere to predefined bounds.

5. Statistical Analysis and Plotting:

   • Provides functionalities for extensive statistical analysis and visualization of results.
   • Generates plots comparing original and sampled data distributions, as well as histograms to visualize the false association rates.

6. Command-Line Interface:

   • Offers a user-friendly command-line interface for easy script execution with options for specifying the database file, criterion type, limit, and kernel bandwidth.
   • Includes flags for checking KDE fit and characterizing results