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.
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.
- 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
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_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).
All of the package dependencies (listed below) are contained within the Anaconda python distribution, except for
For simulations, you will need:
- basic python scientific packages (
To do posterior analysis, you will also need:
To install these two, follow the instructions here.
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.
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_genNR modules, see also here.
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).