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KDE-Detector

Kernel density estimation for 2D observational data, based on Koposov et al 2008. There are two kinds of statistics:

  • 2 Gaussian kernel convolution
  • Poisson distribution

Parameters and Database

Before starting density estimation, one shall first set up:

  • src/param.py: parameters for density estimation
  • src/param_patch_candidate.py: parameters for preprocessing and summary
  • wsdb.py: permission for wsdb.

How to use it:

  1. Clean the work directory: bash bashtools/clean.sh
  2. Preprocess a dwarf list (optional):
    python preprocess.py
  3. Get access to the database and enter information in wsdb.py
  4. Set up parameters in src/param.py, especially the following:
    • IS_DWARF_LIST = False # use joint list
    • IS_DWARF_SPLIT_LIST = True # use joint-split list
  5. Calculate density estimation:
    • python -W ignore main.py
      (if using manual mode)
    • python -W ignore main.py --name_dwarf "Fornax" --gc_size_pc 10
      (if using the joint or joint-split dwarf list: names can be find in the txt files in dwarfs/)
  6. Summarize searching result with python -W ignore summary.py
  7. If running step 4 and 5 on a cluster, slurm job scripts are provided:
    • bash bashtools/slurm-slurm.sh # make sure the right input txt
    • sbatch bashtools/slurm-summary.sh # make sure all the KDE searches are done and then run this command