STATiX (Space and Time Algorithm for Transients in X-rays)

The Space and Time Algorithm for Transients in X-rays (STATiX) builds upon tools from the image and signal processing fields and in particular the Multi-Scale Variance Stabilisation Transform (MSVST; Zhang et al. 2008, Starck et al. 2009) to provide a complete detection analysis pipeline optimised for finding transient sources on X-ray imaging observations. Unlike standard source detection codes, STATiX operates on 3-dimensional data cubes with 2-spatial and one temporal dimensions. It is therefore sensitive to short and faint X-ray flares that may be hidden in the background once the data cube is collapsed in time to produce 2-dimensional images. Although the algorithm is motivated by transient source searches, it also provides a competitive tool for the detection of the general, typically less variable, X-ray source population present in X-ray observations. See Ruiz et al. 2024 for a detailed explanation of the algorithm.

STATiX is distributed as a Python package. The current implementation only allows the processing of data for the XMM-Newton EPIC cameras. Upgrading the code for other X-ray imaging missions is possible, but beyond our current capabilities.

Installation

STATix needs the following software and libraries for a correct installation:

• C/C++ compiler and make
• CMake
• CFITSIO (>V3.31)
• pkg-config

In Ubuntu (and other Debian-based Linux distributions) these dependencies can be installed via apt:

sudo apt install gcc make cmake libcfitsio* pkg-config

Once these prerequisites are installed, STATiX can be easily installed using pip:

pip install xstatix

Although the STATiX source detection pipeline does not need any additional software, some of its side functions related with XMM-Newton data manipulation need a working installation of SAS. If all the initial data products are already available (images, data cubes, exposure maps, etc) SAS is not needed. Otherwise these products will be generated during running time if SAS is available. All SAS-related functions are in the xmmsas module.

Examples

We provide Jupyter notebooks and scripts with examples on how to use STATiX with XMM-Newton data.