21cmFAST

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A semi-numerical cosmological simulation code for the radio 21-cm signal.

joss-paper/yuxiangs-plot-small.png

This is the official repository for 21cmFAST: a semi-numerical code that is able to produce 3D cosmological realisations of many physical fields in the early Universe. It is super-fast, combining the excursion set formalism with perturbation theory to efficiently generate density, velocity, halo, ionization, spin temperature, 21-cm, and even ionizing flux fields (see the above lightcones!). It has been tested extensively against numerical simulations, with excellent agreement at the relevant scales.

21cmFAST has been widely used, for example, by the Murchison Widefield Array (MWA), LOw-Frequency ARray (LOFAR) and Hydrogen Epoch of Reionization Array (HERA), to model the large-scale cosmological 21-cm signal. In particular, the speed of 21cmFAST is important to produce simulations that are large enough (several Gpc across) to represent modern low-frequency observations.

As of v3.0.0, 21cmFAST is conveniently wrapped in Python to enable more dynamic code.

New Features in 3.0.0+

  • Robust on-disk caching/writing both for efficiency and simplified reading of previously processed data (using HDF5).

  • Convenient data objects which simplify access to and processing of the various density and ionization fields.

  • De-coupled functions mean that arbitrary functionality can be injected into the process.

  • Improved exception handling and debugging

  • Comprehensive documentation

  • Comprehensive test suite.

  • Strict semantic versioning.

Installation

We support Linux and MacOS (please let us know if you are successful in installing on Windows!). On these systems, the simplest way to get 21cmFAST is by using conda:

conda install -c conda-forge 21cmFAST

21cmFAST is also available on PyPI, so that pip install 21cmFAST also works. However, it depends on some external (non-python) libraries that may not be present, and so this method is discouraged unless absolutely necessary. If using pip to install 21cmFAST (especially on MacOS), we thoroughly recommend reading the detailed installation instructions.

Basic Usage

21cmFAST can be run both interactively and from the command line (CLI).

Interactive

The most basic example of running a (very small) coeval simulation at a given redshift, and plotting an image of a slice through it:

>>> import py21cmfast as p21c
>>> coeval = p21c.run_coeval(
>>>     redshift=8.0,
>>>     user_params={'HII_DIM': 50, "USE_INTERPOLATION_TABLES": False}
>>> )
>>> p21c.plotting.coeval_sliceplot(coeval, kind='brightness_temp')

The coeval object here has much more than just the brightness_temp field in it. You can plot the density field, velocity field or a number of other fields. To simulate a full lightcone:

>>> lc = p21c.run_lightcone(
>>>     redshift=8.0,
>>>     max_redshift=15.0,
>>>     init_box = coeval.init_struct,
>>> )
>>> p21c.plotting.lightcone_sliceplot(lc)

Here, we used the already-computed initial density field from coeval, which sets the size and parameters of the run, but also means we don’t have to compute that (relatively expensive step again). Explore the full range of functionality in the API Docs, or read more in-depth tutorials for further guidance.

CLI

The CLI can be used to generate boxes on-disk directly from a configuration file or command-line parameters. You can run specific steps of the simulation independently, or an entire simulation at once. For example, to run just the initial density field, you can do:

$ 21cmfast init --HII_DIM=100

The (quite small) simulation box produced is automatically saved into the cache (by default, at ~/21cmFAST-cache). You can list all the files in your cache (and the parameters used in each of the simulations) with:

$ 21cmfast query

To run an entire coeval cube, use the following as an example:

$ 21cmfast coeval 8.0 --out=output/coeval.h5 --HII_DIM=100

In this case all the intermediate steps are cached in the standard cache directory, and the final Coeval box is saved to output/coeval.h5. If no --out is specified, the coeval box itself is not written, but don’t worry – all of its parts are cached, and so it can be rebuilt extremely quickly. Every input parameter to any of the input classes (there are a lot of parameters) can be specified at the end of the call with prefixes of -- (like HII_DIM here). Alternatively, you can point to a config YAML file, eg.:

$ 21cmfast lightcone 8.0 --max-z=15.0 --out=. --config=~/.21cmfast/runconfig_example.yml

There is an example configuration file here that you can build from. All input parameters are documented here.

Documentation

Full documentation (with examples, installation instructions and full API reference) found at https://21cmfast.readthedocs.org.

Acknowledging

If you use 21cmFAST v3+ in your research please cite both of:

Murray et al., (2020). 21cmFAST v3: A Python-integrated C code for generating 3D realizations of the cosmic 21cm signal. Journal of Open Source Software, 5(54), 2582, https://doi.org/10.21105/joss.02582

Andrei Mesinger, Steven Furlanetto and Renyue Cen, “21CMFAST: a fast, seminumerical simulation of the high-redshift 21-cm signal”, Monthly Notices of the Royal Astronomical Society, Volume 411, Issue 2, pp. 955-972 (2011), https://ui.adsabs.harvard.edu/link_gateway/2011MNRAS.411..955M/doi:10.1111/j.1365-2966.2010.17731.x

In addition, the following papers introduce various features into 21cmFAST. If you use these features, please cite the relevant papers.

Mini-halos:

Qin, Y., Mesinger, A., Park, J., Greig, B., and Muñoz, J. B., “A tale of two sites - I. Inferring the properties of minihalo-hosted galaxies from current observations”, Monthly Notices of the Royal Astronomical Society, vol. 495, no. 1, pp. 123–140, 2020. https://doi.org/10.1093/mnras/staa1131.

Mass-dependent ionizing efficiency:

Park, J., Mesinger, A., Greig, B., and Gillet, N., “Inferring the astrophysics of reionization and cosmic dawn from galaxy luminosity functions and the 21-cm signal”, Monthly Notices of the Royal Astronomical Society, vol. 484, no. 1, pp. 933–949, 2019. https://doi.org/10.1093/mnras/stz032.

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