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. 7,8,9,10,11. Default energies are 107,107.25,107.5,…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->35 degrees, in steps of 1 degree.

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

  4. -l or --lepton: flavor of 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. -c or --cdf_only: If set to yes, the output file will not contain outgoing lepton energies, and will only contain exit probabilities and binned outgoing energy CDF values. Default is no.

  12. -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

If you're not sure about the file name format, learn more about wheel file names.

nupyprop-0.1.7.post65-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.9 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

nupyprop-0.1.7.post65-cp39-cp39-macosx_10_9_x86_64.whl (5.0 MB view details)

Uploaded CPython 3.9macOS 10.9+ x86-64

nupyprop-0.1.7.post65-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.9 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ x86-64

nupyprop-0.1.7.post65-cp38-cp38-macosx_10_9_x86_64.whl (5.0 MB view details)

Uploaded CPython 3.8macOS 10.9+ x86-64

nupyprop-0.1.7.post65-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.9 MB view details)

Uploaded CPython 3.7mmanylinux: glibc 2.17+ x86-64

nupyprop-0.1.7.post65-cp37-cp37m-macosx_10_9_x86_64.whl (5.0 MB view details)

Uploaded CPython 3.7mmacOS 10.9+ x86-64

File details

Details for the file nupyprop-0.1.7.post65-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for nupyprop-0.1.7.post65-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 ceae0081946d7c3e98b2b6f4fdcf87c21075c5592195f9e1e4411be58841c98a
MD5 5d8e425abc668c14fe06d0fbb273d557
BLAKE2b-256 592691013e071a9b6d4221f04b367d5378cf51efc0bc8249a76f09c6945f3eb0

See more details on using hashes here.

File details

Details for the file nupyprop-0.1.7.post65-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: nupyprop-0.1.7.post65-cp39-cp39-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 5.0 MB
  • Tags: CPython 3.9, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.6.4 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.1 CPython/3.9.6

File hashes

Hashes for nupyprop-0.1.7.post65-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 3eb4dd7e12adfd8564dc44df8315d77aff27b3b4c2690baaa487f88868130247
MD5 4dd20946e8528bba12c1e7a4d9b30dcb
BLAKE2b-256 f4b7a3140b04a6b6c834f0d59bbb91e6df23b9b7cccb725ce1b8fd006aa07b47

See more details on using hashes here.

File details

Details for the file nupyprop-0.1.7.post65-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for nupyprop-0.1.7.post65-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 3581a8f248d673d7d7cb30a68cf3cdcc4ff1efa96b861438f9c2010abf5688ff
MD5 02cdbf6c12a4208053c0c52e572c8221
BLAKE2b-256 f52ad0bf7d6e24eb41dfef73a170d740ab740c154646c87f58bf321e98a4585e

See more details on using hashes here.

File details

Details for the file nupyprop-0.1.7.post65-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: nupyprop-0.1.7.post65-cp38-cp38-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 5.0 MB
  • Tags: CPython 3.8, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.6.4 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.1 CPython/3.9.6

File hashes

Hashes for nupyprop-0.1.7.post65-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 051eebd08e5056a2e1617b843966450081fb47836adb09712e6580ef11762def
MD5 d6ab6d67fca5bfa75ddb95299cd10eac
BLAKE2b-256 720505a19eee4257516d04dc40712ac315f7897e2bce45b23ab46c10027ed1f5

See more details on using hashes here.

File details

Details for the file nupyprop-0.1.7.post65-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for nupyprop-0.1.7.post65-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 fbf75f2255529b6be94caacbfbaa6708e690bf236f4f6a63d8949bd051ceefd2
MD5 779ef2440a83b31fcd2d20ee7f57fd2d
BLAKE2b-256 40f6927ae24c3fe20894a8862be02ac26ccdc047eac0982e12dda3d381e655dd

See more details on using hashes here.

File details

Details for the file nupyprop-0.1.7.post65-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: nupyprop-0.1.7.post65-cp37-cp37m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 5.0 MB
  • Tags: CPython 3.7m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.6.4 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.1 CPython/3.9.6

File hashes

Hashes for nupyprop-0.1.7.post65-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 a310ad8b37f9e6eea70da599e37b049738e5ed0e89de575951a9420fc307fa6e
MD5 a2c645627f5e66f1b6d3a66657a70234
BLAKE2b-256 a499f90cedfce81e6bf55a38d075fca03609fd52fcd8b5c21ae407f2f0866374

See more details on using hashes here.

Supported by

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