galactic-dtd

Source for Dubay et al. (2024), "Galactic Chemical Evolution Models Favor an Extended Type Ia Supernova Delay-Time Distribution"

https://github.com/lodubay/galactic-dtd

Science Score: 67.0%

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

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 5 DOI reference(s) in README
  • Academic publication links
    Links to: arxiv.org, zenodo.org
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (12.7%) to scientific vocabulary
Last synced: 7 months ago · JSON representation ·

Repository

Source for Dubay et al. (2024), "Galactic Chemical Evolution Models Favor an Extended Type Ia Supernova Delay-Time Distribution"

Basic Info
  • Host: GitHub
  • Owner: lodubay
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 124 MB
Statistics
  • Stars: 2
  • Watchers: 1
  • Forks: 1
  • Open Issues: 0
  • Releases: 3
Created about 4 years ago · Last pushed almost 2 years ago
Metadata Files
Readme License Citation

README.md

showyourwork

Article status Article tarball Article graph Read the article DOI

Welcome to the repository for Dubay et al. (2024), "Galactic Chemical Evolution Models Favor an Extended Type Ia Supernova Delay-Time Distribution", arXiv:2404.08059.

To re-build the article yourself, simply run the following from the repository directory: $ showyourwork build The data and model outputs will automatically be downloaded from Zenodo deposit 10.5281/zenodo.10961090.

To re-create the APOGEE sample with Leung et al. (2023) age estimates, run the following: $ python generate_sample.py This will replace everything in the src/data/APOGEE directory.

To re-run all models yourself, run the following: $ python run_all_models.py This will replace everything in the src/data/multizone directory, including the output files from Zenodo.

Source code for the models is located within the src/scripts/multizone/ directory. To run a single multi-zone model with custom parameters, run the following: $ cd src/scripts $ python -m multizone [OPTIONS...] In particular, code for the stellar migration prescription is contained within the gaussian_migration class located at src/scripts/multizone/src/migration.py.

The figures in the paper do not cover every possible combination of SFH + DTD. The following script generates supplementary plots for every multi-zone model available: $ cd src/scripts $ python extra_plots.py The multi-panel plots show age vs [O/Fe], [O/Fe] vs [Fe/H], MDFs, and [O/Fe] DFs for each Galactic region, plus a plot showing the [O/Fe] bimodality. Plots are located in the src/extra directory with the following structure: <migration scheme>/<sfh model>/<dtd model>/.

Owner

  • Name: Liam Dubay
  • Login: lodubay
  • Kind: user

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you make use of the results in this paper, please cite it as below."
authors:
  - family-names: "Last Name"
    given-names: "First Name"
    orcid: "https://orcid.org/0000-0000-0000-0000"
title: "Repository title"
version: 0.0.0
doi: "N/A"
date-released: "N/A"
url: "N/A"
preferred-citation:
  type: article
  authors:
    - family-names: "Last Name"
      given-names: "First Name"
      orcid: "https://orcid.org/0000-0000-0000-0000"
  doi: "N/A"
  journal: "Journal Name"
  month: "Jan"
  start: 0
  title: "Article title"
  issue: 0
  volume: 0
  year: 0

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Dependencies

.github/workflows/build-pull-request.yml actions
  • actions/checkout v3 composite
  • showyourwork/showyourwork-action v1 composite
.github/workflows/build.yml actions
  • actions/checkout v3 composite
  • showyourwork/showyourwork-action v1 composite
.github/workflows/process-pull-request.yml actions
  • showyourwork/showyourwork-action/process-pull-request v1 composite
environment.yml conda
  • astropy >=5.1
  • dill >=0.2.0
  • jinja2 >=3.1.0
  • numpy >=1.24.0
  • pandas >=1.5.0
  • pip >=23.1.0
  • python 3.10.0.*
  • scikit-learn >=1.2.0
  • tqdm >=4.65.0