SNEWPY: Supernova Neutrino Early Warning Models for Python
SNEWPY is a Python package for working with supernova neutrinos. It offers …
- … a simple and unified interface to hundreds of supernova simulations.
- … a large library of flavor transformations that relate neutrino fluxes produced in the supernova to those reaching a detector on Earth.
- … and a Python interface to SNOwGLoBES which lets you estimate and plot event rates in many different neutrino detectors.
Installation
Run pip install snewpy to install SNEWPY.
SNEWPY includes a large number of supernova models from different simulation groups. Since these models have a size of several 100 MB, they are not included in the initial install but will be downloaded automatically when needed.
Alternatively, you can run the following command to explicitly download models you want to use to a subdirectory named SNEWPY-models/<model_name>/ in the current directory:
python -c 'import snewpy; snewpy.get_models()'
Usage and Documentation
SNEWPY gives you easy access to hundreds of included SN simulations …
import astropy.units as u
from snewpy.models.ccsn import Nakazato_2013, Bollig_2016
# Initialise two SN models. This automatically downloads the required data files if necessary.
nakazato = Nakazato_2013(progenitor_mass=20*u.solMass, revival_time=100*u.ms, metallicity=0.004, eos='shen')
bollig = Bollig_2016(progenitor_mass=27*u.solMass)… and many flavor transformations that neutrinos could experience on the way to Earth …
from snewpy.flavor_transformation import AdiabaticMSW
from snewpy.neutrino import MassHierarchy
# Adiabatic MSW flavor transformation with normal mass ordering
msw_nmo = AdiabaticMSW(mh=MassHierarchy.NORMAL)… letting you quickly calculate the neutrino flux reaching Earth:
times = [0.5, 1] * u.s
energies = range(5,50) * u.MeV
# Assume a SN at the fiducial distance of 10 kpc and normal mass ordering.
flux = bollig.get_flux(times, energies, distance=10*u.kpc, flavor_xform=msw_nmo)You can also calculate the observed event rate in all neutrino detectors supported by SNOwGLoBES, use the included SN models and flavor transformations in third-party code (like event generators), and much more.
Jupyter notebooks showcasing the downloadable supernova models available through SNEWPY and much of its functionality are available in the doc/nb/ subfolder.
Additional example scripts are in the
python/snewpy/scripts/ subfolder.
Papers describing SNEWPY and the underlying physics are published in the Astrophysical Journal (DOI:10.3847/1538-4357/ac350f, arXiv:2109.08188) and the Journal of Open Source Software (DOI:10.21105/joss.03772).
For more, see the full documentation on Read the Docs.
Contributing
Your contributions to SNEWPY are welcome! For minor changes, simply submit a pull request. If you plan larger changes, it’s probably a good idea to open an issue first to coordinate our work.
We use a Fork & Pull Request workflow, which is common on GitHub. Please see the Contributing page in our full documentation for details.