NoiseChisel and Gnuastro: non-parametric detection and analysis of astronomical targets
In this talk, after reviewing the major systematic biases regarding astronomical object detection that is inherent to the signal-based paradigm, I will introduce a fundamentally new noise-based detection paradigm for detecting signals that may have extremely low signal-to-noise ratios. With thresholds that are far below the Sky value, and non-parametric expansion into noise, it is successfully able to detect very diffuse and irregularly shaped signals in noise (e.g., galaxies, comets, etc).
The software implementation is called NoiseChisel. The talk will continue with an introduction to NoiseChisel’s parent software: GNU Astronomy Utilities (or Gnuastro). It is a large package of useful programs and libraries for astronomical data analysis directly on the command-line without the need to use mini-environments like IRAF or Python. It fully conforms with the GNU Coding Standards for easy integration into all Unix-like operating systems (GNU/Linux distros and MacOS for example) with familiar installation, usage and documentation.