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.post106-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.post106-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.post106-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.post106-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.post106-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.post106-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.post106-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for nupyprop-0.1.7.post106-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 76fd5d4f289b72656f40e3fd21ce707e019aea91edabd076b5b77e51f2e20323
MD5 7dc6cdf85347f1f1eba99c291f6941d5
BLAKE2b-256 3ad7d7e99e13db65132fccb89102373e631c66ad5564cf5025a9dca474261dcd

See more details on using hashes here.

File details

Details for the file nupyprop-0.1.7.post106-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: nupyprop-0.1.7.post106-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.post106-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 1e303876b4d2012f7a8856723e2c52d89fdc8257e58951a5d7544e8fa7885273
MD5 f5460bc6c187da6f6cd97b9de16c2ee2
BLAKE2b-256 ae57a2c5394416020f4b14f47fec7200e32e1b56256309c23cb1692cd6edea66

See more details on using hashes here.

File details

Details for the file nupyprop-0.1.7.post106-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for nupyprop-0.1.7.post106-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 bd457753d840b1727933604cf858d1b83c98d266aa92b6bed501a2d30ffb3d7f
MD5 3e348218af0de183a90fba64214813e8
BLAKE2b-256 8ae5d27cbb66b08ee99ea3a38aca5a964f70943e6f935508a3ff492a1bb74289

See more details on using hashes here.

File details

Details for the file nupyprop-0.1.7.post106-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: nupyprop-0.1.7.post106-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.post106-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 0971fdd306af8798767a1905f1068a7a3f618956341340fc6eaa23492b3eb05e
MD5 973759a0f60fb48bf66f21f84e7aeb2e
BLAKE2b-256 5c70691c83a281dd7129181865afe29f12f4b44304c4bbfcb32d1a8938163783

See more details on using hashes here.

File details

Details for the file nupyprop-0.1.7.post106-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for nupyprop-0.1.7.post106-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 137c171d941f6f23890b02d51756ac3591b68c718f57129aa88ff205a66a8d91
MD5 a377cf7fcdff7516ef91e1286c9ea5e9
BLAKE2b-256 602e7008a322e02c98379638ab790a4c5f7a33b999b3b3c91f1a332218617de1

See more details on using hashes here.

File details

Details for the file nupyprop-0.1.7.post106-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: nupyprop-0.1.7.post106-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.post106-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 df6274796d2812f0cbada9fbab110134f3fbee61e918fc77350b4087b786c549
MD5 39c0245d41cb9ade63391f679ad6a8d9
BLAKE2b-256 33d2382a7d447a0457ff5792c9e25f86559adbd20fea0950ea373dc0fd51c7e0

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