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Project Description
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.

``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 ``MultiNest``.

All rate and response functions directly implement the calculations of `Anand et al. (2013) <http: arxiv.org="" abs="" 1308.6288="">`_ and `Fitzpatrick et al. (2013) <https: inspirehep.net="" record="" 1094068?ln="en">`_ (for non-relativistic operators, in ``rate_genNR`` and ``rate_NR``), and `Gresham & Zurek (2014) <http: arxiv.org="" abs="" 1401.3739="">`_ (for UV-motivated scattering models in ``rate_UV``). Simulations follow the prescription from `Gluscevic & Peter (2014) <http: adsabs.harvard.edu="" abs="" 2014jcap...09..040g="">`_ and `Gluscevic et al. (2015) <http: arxiv.org="" abs="" 1506.04454="">`_.


Dependencies
------------

All of the package dependencies (listed below) are contained within the `Anaconda python distribution <http: continuum.io="" downloads="">`_, 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 <http: astrobetter.com="" wiki="" multinest+installation+notes="">`_.


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 <https: github.com="" veragluscevic="" dmdd="" blob="" master="" dmdd_tutorial.ipynb="">`_; for more complete documentation, `read the docs <http: dmdd.rtfd.org="">`_; and for the most important formulas and definitions regarding the ``rate_NR`` and ``rate_genNR`` modules, see also `here <https: github.com="" veragluscevic="" dmdd="" blob="" master="" rate_calculators.pdf="">`_.

Attribution
-----------

This package was originally developed for `Gluscevic et al (2015) <http: arxiv.org="" abs="" 1506.04454="">`_. If you use this code in your research, please cite `this ASCL reference <http: ascl.net="" code="" search="" dmdd="">`_, and the following publications: `Gluscevic et al (2015) <http: arxiv.org="" abs="" 1506.04454="">`_, `Anand et al. (2013) <http: arxiv.org="" abs="" 1308.6288="">`_, `Fitzpatrick et al. (2013) <https: inspirehep.net="" record="" 1094068?ln="en">`_, and `Gresham & Zurek (2014) <http: arxiv.org="" abs="" 1401.3739="">`_.
Release History

Release History

0.2

This version

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0.1.1

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0.1

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Download Files

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
dmdd-0.2.tar.gz (130.7 kB) Copy SHA256 Checksum SHA256 Source Apr 12, 2016

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