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Lunch talk by Mohammad Akhlaghi
12:30-13:00 Fri 23 Oct 2015

Marsfield RPL Basement, ATNF


Title: New noise-based detection and segmentation algorithm for diffuse, irregular, and faint signal

A fundamentally new 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. Therefore it is independent of the dimensionality of the dataset and can be later implemented for 3D data-cubes too. The sub-sky detection threshold is defined and initial detections are found, independently of the sky value. False detections are subsequently 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, namely segmentation, 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. All the scripts and configuration files necessary to exactly reproduce all the data generated numbers and plots in this paper are publicly released (see link) and can be run with a `make' command. NoiseChisel is distributed as part of the new GNU Astronomy Utilities which is the first professional astronomical software to fully comply with the robust GNU Coding Standards and thus integrate finely with Unix-like operating systems and provide familiar command line user interface.

GNU Astronomy Utilities:
Reproduction pipeline:

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Jing Wang

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