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dmdd

A python package that enables simple simulation and Bayesian posterior analysis of nuclear-recoil data from dark matter direct detection experiments for a wide variety of theories of dark matter-nucleon interactions. The code is being actively developed on GitHub; please feel free to contribute pull requests or raise issues.

dmdd has the following features:

  • Calculation of the nuclear-recoil rates for various non-standard momentum-, velocity-, and spin-dependent scattering models.
  • Calculation of the appropriate nuclear response functions triggered by the chosen scattering model.
  • Inclusion of natural abundances of isotopes for a variety of target elements: Xe, Ge, Ar, F, I, Na, He4, He3, Ne, N.
  • Simple simulation of data (where data is a list of nuclear recoil energies, including Poisson noise) under different models.
  • Bayesian analysis (parameter estimation and model selection) of data using MultiNest.

All rate and response functions directly implement the calculations of Anand et al. (2013) and Fitzpatrick et al. (2013) (for non-relativistic operators, in rate_genNR and rate_NR), and Gresham & Zurek (2014) (for UV-motivated scattering models in rate_UV). Simulations follow the prescription from Gluscevic & Peter (2014) and Gluscevic et al. (2015).

Dependencies

All of the package dependencies (listed below) are contained within the Anaconda python distribution, except for MultiNest and PyMultinest.

For simulations, you will need:

  • basic python scientific packages (numpy, scipy, matplotlib)
  • cython

To do posterior analysis, you will also need:

  • MultiNest
  • PyMultiNest

To install these two, follow the instructions here.

Installation

Install dmdd either using pip:

pip install dmdd

or by cloning the repository:

git clone https://github.com/veragluscevic/dmdd.git
cd dmdd
python setup.py install

Note that if you do not set the DMDD_MAIN_PATH environment variable, then importing dmdd will create ~/.dmdd and use that location to store simulations and posterior samples.

Usage

For a quick tour of usage, check out the tutorial notebook; for more complete documentation, read the docs; and for the most important formulas and definitions regarding the rate_NR and rate_genNR modules, see also here.

Basic Usage

Here is a quick example of basic usage:

from dmdd import UV_Model, Experiment, MultinestRun

model1 = UV_Model('SI_Higgs', ['mass', 'sigma_si'], fixed_params={'fnfp_si': 1})
model2 = UV_Model('SD_fu', ['mass','sigma_sd'], fixed_params={'fnfp_sd': -1.1})

xe = Experiment('Xe', 'xenon', 5, 40, 1000, eff.efficiency_Xe)

run = MultinestRun('sim', [xe,ge], model1,{'mass':50.,'sigma_si':70.},
               model2, prior_ranges={'mass':(1,1000), 'sigma_sd':(0.001,1000)})

run.fit()
run.visualize()

Attribution

This package was originally developed for Gluscevic et al (2015). If you use this code in your research, please cite this ASCL reference, and the following publications: Gluscevic et al (2015), Anand et al. (2013), Fitzpatrick et al. (2013), and Gresham & Zurek (2014).

API Documentation

.. toctree::
   :maxdepth: 2

   rates
   api