https://github.com/musevlt/origin

ORIGIN: detectiOn and extRactIon of Galaxy emIssion liNes
https://github.com/musevlt/origin

astronomy astrophysics musevlt python source-detection

Last synced: about 2 months ago
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ORIGIN: detectiOn and extRactIon of Galaxy emIssion liNes

• Host: GitHub
• URL: https://github.com/musevlt/origin
• Owner: musevlt
• License: mit
• Created: 2019-07-03T15:50:47.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2022-05-15T21:42:57.000Z (about 4 years ago)
• Last Synced: 2025-09-22T17:35:46.867Z (8 months ago)
• Topics: astronomy, astrophysics, musevlt, python, source-detection
• Language: Python
• Homepage: https://muse-origin.readthedocs.io
• Size: 80.7 MB
• Stars: 4
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.rst
  • Changelog: CHANGELOG
  • License: LICENSE

Awesome Lists containing this project

README

          
.. image:: https://github.com/musevlt/origin/workflows/Run%20unit%20tests/badge.svg

  :target: https://github.com/musevlt/origin

.. image:: https://codecov.io/gh/musevlt/origin/branch/master/graph/badge.svg

  :target: https://codecov.io/gh/musevlt/origin

ORIGIN is a software to perform blind detection of faint emitters in MUSE

datacubes.

The algorithm is tuned to efficiently detects faint spatial-spectral emission

signatures, while  allowing for a stable false detection rate over the data cube

and providing in the same time an automated and reliable estimation of the

purity.

The algorithm implements :

1. A nuisance removal part based on a continuum subtraction  combining

a Discrete Cosine Transform and an iterative Principal Component Analysis,

2. A detection part based on the local maxima of Generalized Likelihood

Ratio test  statistics obtained for a set of spatial-spectral profiles of

emission line emitters,

3. A purity estimation part, where the proportion of true emission lines

is estimated from the data itself:  the distribution of the local maxima in

the noise only configuration is estimated from that of the local minima.

Citation

--------

ORIGIN is presented in the following paper:

`Mary et al., A&A, 2020 `_

Links

-----

- `Documentation `_

- `PyPI `_

- `Github `_