Skip to main content

A simulator for neutrino propagation through the earth.

Project description

Propagate neutrinos through the earth.

A python package and command line utility, including fortran for performance with openMP.

Documentation (WIP): https://nupyprop.readthedocs.io/en/latest/

Note: While the documentation is currently WIP, users and developers should consult the nuPyProp tutorial repository for visualizing output from the code and creating user-defined models.

Installation

with pip

python3 -m pip install nupyprop

with conda

We recommend installing nupyprop into a conda environment like so. In this example the name of the environment is “nupyprop”

conda create -n nupyprop -c conda-forge -c nuspacesim nupyprop
conda activate nupyprop

Usage

nupyprop --help

Example for running tau propagation for 107 GeV neutrinos at 10 degrees with 107 neutrinos injected with stochastic energy loss & with all other parameters as defaults:

nupyprop -e 7 -a 10 -t stochastic -s 1e7

Run parameters are defined in run.py. Different switches are described as follows:

  1. -e or --energy: incoming neutrino energy in log_10(GeV). Works for single energy or multiple energies. For multiple energies, separate energies with commas eg. 6,7,8,9,10,11. Default energies are 106,106.25,106.5,…1011 GeV.

  2. -a or --angle: slant Earth angles in degrees. Works for single angle or multiple angles. For multiple angles, separate angles with commas eg. 1,3,5,7,10. Default angles are 1->42 degrees, in steps of 1 degree.

  3. -i or --idepth: depth of ice/water in km. Default value is 4 km.

  4. -cl or --charged_lepton: flavor of charged lepton used to propagate. Can be either muon or tau. Default is tau.

  5. -n or --nu_type: type of neutrino matter. Can be either neutrino or anti-neutrino. Default is neutrino.

  6. -t or --energy_loss: energy loss type for lepton - can be stochastic or continuous. Default is stochastic.

  7. -x or --xc_model: neutrino/anti-neutrino cross-section model used. Can be from the pre-defined set of models (see xc-table) or custom. Default is ct18nlo.

  8. -p or --pn_model: photonuclear interaction energy loss model used. Can be from the pre-defined set of models (see pn-table) or custom. Default is allm.

  9. -f or --fac_nu: rescaling factor for BSM cross-sections. Default is 1.

  10. -s or --stats: statistics or no. of injected neutrinos. Default is 1e7 neutrinos.

  11. -htc or --htc_mode: High throughput computing mode. If set to yes, the code will be optimized to run in high throughput computing mode. Default is no.

Viewing output results: output_*.h5 will contain the results of the code after it has finished running. In the terminal, run vitables (optional dependency) and open the output_*.h5 file to view the output results.

output_*.h5 naming convention is as follows: output_A_B_C_D_E_F_G, where

A = Neutrino type: nu is for neutrino & anu is for anti-neutrino.
B = Lepton_type: tau is for tau leptons & muon is for muons.
C = idepth: depth of water layer, in km.
D = Neutrino (or anti-neutrino) cross-section model.
E = Charged lepton photonuclear energy loss model.
F = Energy loss type: can be stochastic or continuous.
G = Statistics (ie. no. of neutrinos/anti-neutrinos injected).

Model Tables

Neutrino/Anti-Neutrino Cross-Section Model

Reference

Abramowicz, Levin, Levy, Maor (ALLM)

hep-ph/9712415, Phys. Rev. D 81, 114012

Block, Durand, Ha, McKay (BDHM)

Phys. Rev. D 89, 094027, Phys. Rev. D 81, 114012

CTEQ18-NLO

Phys. Rev. D 103, 014013, Phys. Rev. D 81, 114012

Connolly, Thorne, Waters (CTW)

Phys. Rev. D 83, 113009

nCTEQ15

Phys. Rev. D 93, 085037, Phys. Rev. D 81, 114012

User Defined

See nuPyProp tutorial repository

Charged Lepton Photonuclear Energy Loss Model

Reference

Abramowicz, Levin, Levy, Maor (ALLM)

hep-ph/9712415, Phys. Rev. D 63, 094020

Bezrukov-Bugaev (BB)

Yad. Fiz. 33, 1195, Phys. Rev. D 63, 094020

Block, Durand, Ha, McKay (BDHM)

Phys. Rev. D 89, 094027, Phys. Rev. D 63, 094020

Capella, Kaidalov, Merino, Tran (CKMT)

Eur. Phys. J. C 10, 153 Phys. Rev. D 63, 094020

User Defined

See nuPyProp tutorial repository

Code Execution Timing Tables

Charged Lepton

Energy Loss Type

E|nu| [GeV]

Angles

N|nu|;;in

Time (hrs)

τ

Stochastic

107

1-35

108

1.07*, 0.26***

τ

Continuous

107

1-35

108

0.88*

τ

Stochastic

108

1-35

108

6.18*, 1.53***

τ

Continuous

108

1-35

108

5.51*

τ

Stochastic

109

1-35

108

27.96*, 5.08***

τ

Continuous

109

1-35

108

19.11*

τ

Stochastic

1010

1-35

108

49.80*, 12.43***

τ

Continuous

1010

1-35

108

35.59*

τ

Stochastic

1011

1-35

108

12.73***

τ

Continuous

1011

1-35

108

Charged Lepton

Energy Loss Type

E|nu| [GeV]

Angles

N|nu|;;in

Time (hrs)

μ

Stochastic

106

1,2,3,5,7,10,12,15,17,20,25,30,35

108

μ

Continuous

106

1,2,3,5,7,10,12,15,17,20,25,30,35

108

0.95*

μ

Stochastic

107

1,2,3,5,7,10,12,15,17,20,25,30,35

108

μ

Continuous

107

1,2,3,5,7,10,12,15,17,20,25,30,35

108

3.19*

μ

Stochastic

108

1,2,3,5,7,10,12,15,17,20,25,30,35

108

μ

Continuous

108

1,2,3,5,7,10,12,15,17,20,25,30,35

108

5.17*

μ

Stochastic

109

1,2,3,5,7,10,12,15,17,20,25,30,35

108

111.77**

μ

Continuous

109

1,2,3,5,7,10,12,15,17,20,25,30,35

108

7.42*

μ

Stochastic

1010

1,2,3,5,7,10,12,15,17,20,25,30,35

108

98.17*

μ

Continuous

1010

1,2,3,5,7,10,12,15,17,20,25,30,35

108

9.76*

μ

Stochastic

1011

1,2,3,5,7,10,12,15,17,20,25,30,35

108

μ

Continuous

1011

1,2,3,5,7,10,12,15,17,20,25,30,35

108

* - Intel Core i7-8750H; 6 cores & 12 threads. ** - Intel Core i5-10210; 4 cores & 8 threads. *** - UIowa Argon cluster; 56 cores.

For debugging/development: The correct order to look at the code is in the following order:

  1. data.py: contains functions for reading/writing from/to hdf5 files.

  2. geometry.py: contains the Earth geometry modules (including PREM) for use with python/fortran.

  3. models.py: contains neutrino cross-section & charged lepton energy loss model templates.

  4. propagate.f90: heart of the code; contains fortran modules to interpolate between geometry variables, cross-sections, energy loss parameters & propagate neutrinos and charged leptons through the Earth.

  5. main.py: forms the main skeleton of the code; propagates the neutrinos and charged leptons, and calculates the p_exit and collects outgoing lepton energies.

  6. run.py: contains all the run parameters and variables needed for all the other .py files.

Developing the code on Ubuntu

These notes should help developers of this code build and install the package locally using a pep518 compliant build system (pip).

  1. Install the non-pypi required dependencies as described for users above.

  2. Install a fortran compiler. ex: sudo apt-get install gfortran

  3. git clone the source code: git clone git@github.com:NuSpaceSim/nupyprop.git

  4. cd nupyprop

  5. build and install the package in ‘editable’ mode python3 -m pip install -e .

Developing the code on MacOS

These notes should help developers of this code build and install the package locally using a pep518 compliant build system (pip). Currently we do not support the default system python3 on MacOS which is out of date and missing critical functionality. Use the homebrew python instead, or a virtualenv, or a conda environment.

  1. Install the non-pypi required dependencies as described for users above.

  2. Install a fortran compiler. ex: brew install gcc

  3. git clone the source code: git clone git@github.com:NuSpaceSim/nupyprop.git

  4. cd nupyprop

  5. build and install the package in ‘editable’ mode python3 -m pip install -e .

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

nupyprop-0.1.7.post107-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.4 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

nupyprop-0.1.7.post107-cp39-cp39-macosx_10_9_x86_64.whl (16.5 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

nupyprop-0.1.7.post107-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.4 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

nupyprop-0.1.7.post107-cp38-cp38-macosx_10_9_x86_64.whl (16.5 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

nupyprop-0.1.7.post107-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.4 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ x86-64

nupyprop-0.1.7.post107-cp37-cp37m-macosx_10_9_x86_64.whl (16.5 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

File details

Details for the file nupyprop-0.1.7.post107-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for nupyprop-0.1.7.post107-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 48027ad2230725947c8ff152b4e8ed04cb934eaa201c38334da4d135872ce44d
MD5 7510e66f1b5158264ec424956411d414
BLAKE2b-256 c73fffe31826afbfa82b3eecb40af52461f4f1011d18cf9829c24fcf71c52310

See more details on using hashes here.

File details

Details for the file nupyprop-0.1.7.post107-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: nupyprop-0.1.7.post107-cp39-cp39-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 16.5 MB
  • Tags: CPython 3.9, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.9.7

File hashes

Hashes for nupyprop-0.1.7.post107-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 aecfbfcc6d866db0635fd467e748758170ed0da90294a35a68a76068c61c6650
MD5 ffc2d457d91336c746e37f01b18c3e42
BLAKE2b-256 9259c98952418ebed510ae9023ad7eddeb58b623899f1dce6d6865981edf399f

See more details on using hashes here.

File details

Details for the file nupyprop-0.1.7.post107-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for nupyprop-0.1.7.post107-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 a825aba047dc55eb1064a70ba0f9e30bc0e35f1e3a1456a730c6e121929a2ff5
MD5 714eb2794999df417349d20a5c1cc5b7
BLAKE2b-256 7afc5794e927deb8a02b21d37df1e697cad56569279983c1fb8e5750dba674b9

See more details on using hashes here.

File details

Details for the file nupyprop-0.1.7.post107-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: nupyprop-0.1.7.post107-cp38-cp38-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 16.5 MB
  • Tags: CPython 3.8, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.9.7

File hashes

Hashes for nupyprop-0.1.7.post107-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 117774c4c303c0c4905ff1a80d2a9ea793ca9e6708c8b9f9ab782902e854160d
MD5 5a1b787d3a85933267c72d7334dc2de2
BLAKE2b-256 06b3d54bd059ea322bcf5f1da53e0433b340b78878115e2367f642bc8267f3b0

See more details on using hashes here.

File details

Details for the file nupyprop-0.1.7.post107-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for nupyprop-0.1.7.post107-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 00c7e880a3624a03aaea6eb1b01cac48944bc9b28607689b1dd36d3c99687254
MD5 3f2de8967a1764f7417379ed695c2f19
BLAKE2b-256 a0a278760ce429db8314f4a3be6ec1b2cc37cde017698c8189fc81a3f9d5c366

See more details on using hashes here.

File details

Details for the file nupyprop-0.1.7.post107-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: nupyprop-0.1.7.post107-cp37-cp37m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 16.5 MB
  • Tags: CPython 3.7m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.9.7

File hashes

Hashes for nupyprop-0.1.7.post107-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 1500d649bfed2f191c736eed5701e4d8a851dc74d7c2825535b38a6f5bbaffb9
MD5 101ef390ca219e8d1d236464a9f5a98e
BLAKE2b-256 1c96269f4ef8f04dfd5e1ff6c194a9f358d6cd40e2fcc2eae7ddc8bd13fa1e5f

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page