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km3net-arca-irf

https://github.com/km3net/km3net-arca-irf

Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (13.6%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: KM3NeT
  • License: bsd-3-clause
  • Language: Jupyter Notebook
  • Default Branch: master
  • Size: 1.45 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 3
Created about 2 years ago · Last pushed about 2 years ago
Metadata Files
Readme License Codemeta

README.md

License

KM3NeT/ARCA230 Instrument response functions

This repository contains the instrument response functions of the full KM3NeT/ARCA230 detector. The IRFs are accompanied by a set of scripts to interact with the IRFs and to perform an example cut-and-count analysis to calculate the sensitivity and discovery potential to a neutrino point source.

N.B.: The resulting sensitivity and discovery potential is worse than presented in the paper due to: * The cut-and-count method only looks at the track (or shower) channel instead of combining both, * This analysis only includes signal from $\nu\mu$ and $\bar{\nu}\mu$ CC events selected as track and $\nue$ and $\bar{\nu}e$ CC events selected as shower, instead of all flavours and interactions, * The paper uses a more sophisticated method than presented here. The paper uses a binned likelihood method and throws pseudo experiments to determine the sensitivity, while in this example we use Poisson statistics for a simple counting experiment.

Content

  • data/: Instrument Response Functions (IRFs) for the KM3NeT/ARCA230 detector.
  • analysis/: Jupyter notebooks with example plots and analysis
  • src/arca230/:
    • flux.py: Class that represents a single power law neutrino point source flux.
    • aeff.py: Class that loads the effective area and calculates event rates using a point source flux.
    • psf.py: Class that loads the point spread function and calculates probabilities to reconstruct events with a specified search cone size.
    • energyresponse.py: Class that loads the energy response and convolves true neutrino energies with the energy response of the detector.
    • background.py: Class that calculates expected background rates at different positions in the sky.

Installation

Download

The content of the repository is downloaded via git:

sh git clone git@git.km3net.de:open-data/public-candidates/open-point-source-search.git

Creating the environment

Using venv

Create a virtual environment

sh python -m venv my_venv

Source the virtual environment

sh source my_venv/bin/activate

Install

Enter the dowloaded repository sh cd open-point-source-search and install the requirements

sh pip install -e .

Running Jupyter notebooks

In order to run the notebooks, you need to install Jupyter, by using pip install jupyter or following the instructions at the Juypter website.

Running the Jupyter kernel

From within your virtual environment, create a Jupyter kernel and launch your notebook: sh python -m ipykernel install --user --name=km3net_ps jupyter-notebook

For zsh shell, you need to execute these lines first before installation of the kernel zsh conda install -c conda-forge notebook conda install -c conda-forge nb_conda_kernels

You can then execute the notebooks in your browser following the URL in the stdout.

Owner

  • Name: KM3NeT
  • Login: KM3NeT
  • Kind: organization

Inofficial collection of open source KM3NeT software

CodeMeta (codemeta.json) 

{
  "@context": "https://doi.org/10.5063/schema/codemeta-2.0",
  "@type": "SoftwareSourceCode",
  "license": "https://spdx.org/licenses/BSD-3-Clause",
  "codeRepository": "https://github.com/KM3NeT/KM3NeT-ARCA-irf",
  "dateCreated": "12-02-2024",
  "datePublished": "09-04-2024",
  "dateModified": "10-04-2024",
  "downloadUrl": "https://github.com/KM3NeT/KM3NeT-ARCA-irf.git",
  "issueTracker": "https://github.com/KM3NeT/KM3NeT-ARCA-irf/issues",
  "name": "KM3NeT/ARCA230 instrument response functions and point source analysis.",
  "version": "1.0.1",
  "description": "This repository contains the instrument response functions of the KM3NeT/ARCA230 detector alongside an example analysis for studying the sensitivity to neutrino point sources.",
  "applicationCategory": "Astroparticle physics",
  "releaseNotes": "Version published with paper: Astronomy potential of KM3NeT/ARCA",
  "referencePublication": "Astronomy potential of KM3NeT/ARCA, https://arxiv.org/abs/2402.08363",
  "developmentStatus": "active",
  "readme": "https://github.com/KM3NeT/KM3NeT-ARCA-irf/blob/master/README.md",
  "softwareVersion": "1.0.1",
  "keywords": [
    "KM3NeT",
    "ARCA",
    "Instrument response functions",
    "neutrinos",
    "point source"
  ],
  "programmingLanguage": [
    "Python 3"
  ],
  "operatingSystem": [
    "Linux",
    "Windows"
  ],
  "softwareRequirements": [
    "Python 3.8",
    "gammapy 0.17"
  ],
  "author": [
    {
      "@type": "Person",
      "@id": "https://orcid.org/0000-0003-4980-044X",
      "givenName": "Thijs",
      "familyName": "van Eeden",
      "email": "thijsvaneeden@gmail.com",
      "affiliation": {
        "@type": "Organization",
        "name": "Nikhef"
      }
    }
  ],
  "maintainer": [
    {
      "@type": "Organization",
      "@id": "https://km3net.org",
      "name": "KM3NeT Collaboration",
      "email": "opendata@km3net.de",
      "url": "http://openscience.km3net.de"
    }
  ]
}

GitHub Events

Total
Last Year

Committers

Last synced: over 1 year ago

All Time
  • Total Commits: 3
  • Total Committers: 2
  • Avg Commits per committer: 1.5
  • Development Distribution Score (DDS): 0.333
Past Year
  • Commits: 3
  • Committers: 2
  • Avg Commits per committer: 1.5
  • Development Distribution Score (DDS): 0.333
Top Committers
Name Email Commits
Thijs van Eeden t****n@M****l 2
Thijs van Eeden t****n@d****l 1
Committer Domains (Top 20 + Academic)
dhcp-132-150.nikhef.nl: 1

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Last synced: about 1 year ago

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  • Total pull requests: 0
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  • Average comments per issue: 0
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  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
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