https://github.com/johannesbuchner/bexvar
Bayesian excess variance for Poisson data time series with backgrounds.
https://github.com/johannesbuchner/bexvar
Last synced: 4 months ago
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Bayesian excess variance for Poisson data time series with backgrounds.
- Host: GitHub
- URL: https://github.com/johannesbuchner/bexvar
- Owner: JohannesBuchner
- License: agpl-3.0
- Created: 2021-06-15T08:36:09.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-09-24T16:57:27.000Z (about 1 year ago)
- Last Synced: 2025-05-26T10:02:36.096Z (5 months ago)
- Language: Python
- Homepage:
- Size: 140 KB
- Stars: 6
- Watchers: 4
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.rst
- Changelog: HISTORY.rst
- License: COPYING
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README
bexvar
==================
Bayesian excess variance for Poisson data time series with backgrounds.
Excess variance is over-dispersion beyond the observational poisson noise,
caused by an astrophysical source.
* `Introduction <#introduction>`_
* `Method <#method>`_
* `Tutorial <#tutorial>`_
* `Output plot <#visualising-the-results>`_ and files
Introduction
-------------------
In high-energy astrophysics, the analysis of photon count time series
is common. Examples include the detection of gamma-ray bursts,
periodicity searches in pulsars, or the characterisation of
damped random walk-like accretion in the X-ray emission of
active galactic nuclei.
Methods
--------------
paper: https://arxiv.org/abs/2106.14529
This repository provides new statistical analysis methods for light curves.
They can deal with
* very low count statistics (0 or a few counts per time bin)
* (potentially variable) instrument sensitivity
* (potentially variable) backgrounds, measured simultaneously in an 'off' region.
The tools can read eROSITA light curves. Contributions that can read other
file formats are welcome.
The `bexvar_ero.py` tool computes posterior distributions on the Bayesian excess variance,
and source count rate.
`quick_ero.py` computes simpler statistics, including Bayesian blocks,
fraction variance, the normalised excess variance, and
the amplitude maximum deviation statistics.
Licence
--------
AGPLv3 (see COPYING file). Contact me if you need a different licence.
Install
--------
.. image:: https://img.shields.io/pypi/v/bexvar.svg
:target: https://pypi.python.org/pypi/bexvar
.. image:: https://github.com/JohannesBuchner/bexvar/actions/workflows/test.yml/badge.svg
:target: https://github.com/JohannesBuchner/bexvar/actions/workflows/test.yml
.. image:: https://img.shields.io/badge/astroph.HE-arXiv%3A2106.14529-B31B1B.svg
:target: https://arxiv.org/abs/2106.14529
:alt: Publication
Install as usual::
$ pip3 install bexvar
This also installs the required `ultranest `_
python package.
Example
----------
Run with::
$ bexvar_ero.py 020_LightCurve_00001.fits
Run simpler variability analyses with::
$ quick_ero.py 020_LightCurve_*.fits.gz
Contributing
--------------
Contributions are welcome. Please open pull requests
with code contributions, or issues for bugs and questions.
Contributors include:
* Johannes Buchner
* David Bogensberger
If you use this software, please cite this paper: https://arxiv.org/abs/2106.14529
See also
--------
* https://github.com/rarcodia/eRebin for rebinning eROSITA light curves to eroDays