bexvar 1.1.1

Bayesian excess variance for Poisson data time series with backgrounds.

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

• Method

• Tutorial

• Output plot 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

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

Changelog