https://github.com/lopez86/pywimps

Python tools for dark matter direct detection simulation and analysis. Most well-developed project currently on my account.
https://github.com/lopez86/pywimps

astrophysics dark-matter monte-carlo nuclear-physics particle-physics physics physics-analysis physics-simulation python science simulation

Last synced: about 6 hours ago
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Python tools for dark matter direct detection simulation and analysis. Most well-developed project currently on my account.

• Host: GitHub
• URL: https://github.com/lopez86/pywimps
• Owner: lopez86
• License: mit
• Created: 2017-06-01T01:59:12.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-11-01T17:55:34.000Z (about 7 years ago)
• Last Synced: 2024-01-30T04:07:25.736Z (9 months ago)
• Topics: astrophysics, dark-matter, monte-carlo, nuclear-physics, particle-physics, physics, physics-analysis, physics-simulation, python, science, simulation
• Language: Python
• Homepage:
• Size: 807 KB
• Stars: 2
• Watchers: 1
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# PyWIMPs

This package contains various python tools for simulation & analysis

of dark matter direct detection experiments. If you're a member of the 

HEP or astro community and might want to contribute something, let me know.

## Requirements

So far, the following dependencies are needed:

 * Python 3 (likely 3.4 or above) - primary language

 * NumPy - numerical calculations

 * AstroPy - astrophysics libraries (coordinate transforms)

 * SciPy - used for various statistics things

For the examples, you will also need (depending on the specific example):

 * Matplotlib

 * PyROOT (ROOT with Python bindings)

 * Basemap - extra map plotting tools for Matplotlib

## Features

 * Standard dark matter-nucleus interaction model:

   * Standard Halo Model: Truncated Maxwellian velocity distribution

   * Isotropic cross section

   * Various form factors

   * Nucleus to nucleon normalization

 * Monte Carlo simulation of recoils using the standard halo and cross section assumptions

   * Weighted sampling for building histograms and distributions, calculating weights, etc. (one throwing uniformly over a region and another drawing from a Maxwell-Boltzmann distribution)

   * Un-weighted event-by-event sampling using (1) a basic rejection sampling method and (2) a Markov Chain Monte Carlo (MCMC) using the Metropolis-Hastings algorithm.

 * Some basic limit setting for a simple counting analysis:

   * Background-free counting

   * Feldman-Cousins confidence intervals

   * CLs limits

 * Detector effects: very basic classes for:

   * Efficiency curves

   * Reconstruction effects

   * Realistically, the user will need to make custon classes for their experiment

 * Examples:

   * Running threaded processes

   * Annual modulation curves

   * Limit plot generation 

   * Comparison of sampling methods

   * MCMC tuning

## Future Features

 * Data for common nuclei

 * More limit setting stuff

   * Maximum Gap (Yellen)

   * Annual modulation limits

   * Bayesian limits

   * Parameter fitting for positive results

   * Detector/model systematics treatment (easier in Bayesian case?)

 * Examples of various plots and calculations

   * Recoil distribution skymaps

   * Sidereal modulation skymaps

 * References and readings on dark matter

 * Maybe/Might be fun

   * Simplified parameterized simulation of a LUX or XENON type detector

   * Inelastic dark matter

   * Q^2-dependent cross sections

   * Coherent neutrino elastic scattering