Noise Based Detection and Segmentation of Nebulous Objects
  A noise-based non-parametric technique for detecting nebulous objects, for
example, irregular or clumpy galaxies, and their structure in noise is
introduced. "Noise-based" and "non-parametric" imply that this technique
imposes negligible constraints on the properties of the targets and that it
employs no regression analysis or fittings. The sub-sky detection threshold is
defined and initial detections are found, independently of the sky value. False
detections are then estimated and removed using the ambient noise as a
reference. This results in a purity level of 0.89 for the final detections as
compared to 0.29 for SExtractor when a completeness of 1 is desired for a
sample of extremely faint and diffuse mock galaxy profiles. The difference in
the mean of the undetected pixels with the known background of mock images is
decreased by 4.6 times depending on the diffuseness of the test profiles,
quantifying the success in their detection. A non-parametric approach to
defining substructure over a detected region is also introduced. NoiseChisel is
our software implementation of this new technique. Contrary to the existing
signal-based approach to detection, in its various implementations,
signal-related parameters such as the image point spread function or known
object shapes and models are irrelevant here. Such features make this technique
very useful in astrophysical applications such as detection, photometry, or
morphological analysis of nebulous objects buried in noise, for example,
galaxies that do not generically have a known shape when imaged.
