Skip to main content

Constraining Macro Dark Matter Models with Lightning.

Project description

Welcome to macro_lightning, the code-base for a paper on constraining macroscopic dark matter models with observations of lightning on Earth and Jupiter. If you are looking for the paper, the peer-reviewed journal article will be linked here and the arXiv preprint here. Alternatively, the source code for the paper is included as a sub-module in the folder “papers_and_presentations/paper”.

Macroscopic dark matter (macros) is a broad class of alternative candidates to particle dark matter. These candidates would transfer energy to matter primarily through elastic scattering. A sufficiently large macro passing through the atmosphere would produce a straight channel of ionized plasma. If the cross-section of the macro is \sigma_x \gtrapprox 6 \times 10^{-9} \rm{cm}^2, then under atmospheric conditions conducive to lightning (eg. a thunderstorm) the plasma channel would be sufficient to seed a lightning strike with a single leader.

This is entirely unlike ordinary bolt lightning in which a long sequence of hundreds or thousands of few-meter-long leaders are strung together. This macro-induced lightning would be extremely straight, and thus highly distinctive. Neither wind shear nor magnetohydrodynamic instabilities would markedly spoil its straightness. The only photographically documented case of a straight lightning bolt is probably not straight enough to have been macro-induced.

For any discussion or derivations, see thee paper. For code documentation, see ReadTheDocs. This is the raw code.

DOI PyPI Build Status Coveralls astropy

Notebooks

Folder contains Mathematica notebooks to compute event rates for macro-induced lightning, as well as the constraints in mass and cross-section, both for lightning events on Earth and on Jupiter. There is also a notebook to compute the fraction of the Maxwell distribution of DM particles with velocities high enough to produce straight lightning bolts (ie. traveling faster than a lightning leader would ordinarily propagate).

Further notebooks can be found in “docs/examples”

Papers and Presentations

Look in folder for paper

CODE

The code is included in the macro_lightning folder.

References

Many of the sources cited in the paper are downloaded and included here.

How to contribute

GitHub milestones GitHub issues GitHub last commit (branch)

We welcome contributions from anyone via pull requests on GitHub. If you don’t feel comfortable modifying or adding functionality, we also welcome feature requests and bug reports as GitHub issues.

The development process follows that of the astropy-package-template from Astropy’s release procedure.

Attribution

DOI License

Copyright 2020 - Nathaniel Starkman, Jagjit Sidhu, Harrison Winch, Glenn Starkan, and contributors.

macro_lightning is free software made available under the BSD-3 License. For details see the LICENSE file.

If you make use of this code, please consider citing the Zenodo DOI as a software citation

@software{macro_lightning:zenodo,
  author       = {Nathaniel Starkman and Jagjit Sidhu and Harrison Winch and Glenn Starkman},
  title        = "Constraints from Macro-Induced Lightning",
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.3911476},
  url          = {https://zenodo.org/badge/latestdoi/275470390}
}

Project details


Download files

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

Source Distribution

macro_lightning-1.0.tar.gz (38.1 MB view details)

Uploaded Source

File details

Details for the file macro_lightning-1.0.tar.gz.

File metadata

  • Download URL: macro_lightning-1.0.tar.gz
  • Upload date:
  • Size: 38.1 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.24.0 setuptools/47.3.1.post20200616 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.7.4

File hashes

Hashes for macro_lightning-1.0.tar.gz
Algorithm Hash digest
SHA256 0829751b98e0fb649592824fb2db53f46e5c2260bb45481347a416df3354fbf6
MD5 082da808a4210378acabf327cb9607da
BLAKE2b-256 dd286e626776d665f39d09d10ac8c4b6fa16e6026e2bf9c512b60fa004570bbb

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