The options below can be used to configure every step of the segmentation process in the Segment program. For a more complete explanation (with figures to demonstrate each step), please see Section 3.2 of Akhlaghi and Ichikawa , and also Segment. By default, Segment will follow the procedure described in the paper to find the S/N threshold based on the noise properties. This can be disabled by directly giving a trustable signal-to-noise ratio to the --clumpsnthresh option.
Minimum fraction (value between 0 and 1) of Sky (undetected) areas in a large tile. Only (large) tiles with a fraction of undetected pixels (Sky) greater than this value will be used for finding clumps. The clumps found in the undetected areas will be used to estimate a S/N threshold for true clumps. Therefore this is an important option (to decrease) in crowded fields. Operationally, this is almost identical to NoiseChisel’s --minskyfrac option (Detection options). Please see the descriptions there for more.
Build the clumps based on the local minima, not maxima. By default, clumps are built starting from local maxima (see Figure 8 of Akhlaghi and Ichikawa ). Therefore, this option can be useful when you are searching for true local minima (for example, absorption features).
The minimum area which a clump in the undetected regions should have in order to be considered in the clump Signal to noise ratio measurement. If this size is set to a small value, the Signal to noise ratio of false clumps will not be accurately found. It is recommended that this value be larger than the value to NoiseChisel’s --snminarea. Because the clumps are found on the convolved (smoothed) image while the pseudo-detections are found on the input image. You can use --checksn and --checksegmentation to see if your chosen value is reasonable or not.
Save the S/N values of the clumps over the sky and detected regions into separate tables. If --tableformat is a FITS format, each table will be written into a separate extension of one file suffixed with _clumpsn.fits. If it is plain text, a separate file will be made for each table (ending in _clumpsn_sky.txt and _clumpsn_det.txt). For more on --tableformat see Input/Output options.
You can use these tables to inspect the S/N values and their distribution (in combination with the --checksegmentation option to see where the clumps are). You can use Gnuastro’s Statistics to make a histogram of the distribution (ready for plotting in a text file, or a crude ASCII-art demonstration on the command-line).
With this option, Segment will abort as soon as the two tables are created. This allows you to inspect the steps leading to the final S/N quantile threshold, this behavior can be disabled with --continueaftercheck.
The minimum number of clumps over undetected (Sky) regions to identify the requested Signal-to-Noise ratio threshold. Operationally, this is almost identical to NoiseChisel’s --minnumfalse option (Detection options). Please see the descriptions there for more.
The quantile of the signal-to-noise ratio distribution of clumps in undetected regions, used to define true clumps. After identifying all the usable clumps in the undetected regions of the dataset, the given quantile of their signal-to-noise ratios is used to define the signal-to-noise ratio of a “true” clump. Effectively, this can be seen as an inverse p-value measure. See Figure 9 and Section 3.2.1 of Akhlaghi and Ichikawa  for a complete explanation. The full distribution of clump signal-to-noise ratios over the undetected areas can be saved into a table with --checksn option and visually inspected with --checksegmentation.
Keep a clump whose maximum (minimum if --minima is called) flux is 8-connected to a river pixel. By default such clumps over detections are considered to be noise and are removed irrespective of their significance measure (see Akhlaghi 2019). Over large profiles, that sink into the noise very slowly, noise can cause part of the profile (which was flat without noise) to become a very large and with a very high Signal to noise ratio. In such cases, the pixel with the maximum flux in the clump will be immediately touching a river pixel.
The signal-to-noise threshold for true clumps. If this option is given, then the segmentation options above will be ignored and the given value will be directly used to identify true clumps over the detections. This can be useful if you have a large dataset with similar noise properties. You can find a robust signal-to-noise ratio based on a (sufficiently large) smaller portion of the dataset. Afterwards, with this option, you can speed up the processing on the whole dataset. Other scenarios where this option may be useful is when, the image might not contain enough/any Sky regions.
Threshold (multiple of the sky standard deviation added with the sky) to stop growing true clumps. Once true clumps are found, they are set as the basis to segment the detected region. They are grown until the threshold specified by this option.
The minimum length of a river between two grown clumps for it to be considered in signal-to-noise ratio estimations. Similar to --snminarea, if the length of the river is too short, the signal-to-noise ratio can be noisy and unreliable. Any existing rivers shorter than this length will be considered as non-existent, independent of their Signal to noise ratio. The clumps are grown on the input image, therefore this value can be smaller than the value given to --snminarea. Recall that the clumps were defined on the convolved image so --snminarea should be larger.
The maximum Signal to noise ratio of the rivers between two grown clumps in order to consider them as separate ‘objects’. If the Signal to noise ratio of the river between two grown clumps is larger than this value, they are defined to be part of one ‘object’. Note that the physical reality of these ‘objects’ can never be established with one image, or even multiple images from one broad-band filter. Any method we devise to define ‘object’s over a detected region is ultimately subjective.
Two very distant galaxies or satellites in one halo might lie in the same line of sight and be detected as clumps on one detection. On the other hand, the connection (through a spiral arm or tidal tail for example) between two parts of one galaxy might have such a low surface brightness that they are broken up into multiple detections or objects. In fact if you have noticed, exactly for this purpose, this is the only Signal to noise ratio that the user gives into NoiseChisel. The ‘true’ detections and clumps can be objectively identified from the noise characteristics of the image, so you do not have to give any hand input Signal to noise ratio.
A file with the suffix _seg.fits will be created. This file keeps all the relevant steps in finding true clumps and segmenting the detections into multiple objects in various extensions. Having read the paper or the steps above. Examining this file can be an excellent guide in choosing the best set of parameters. Note that calling this function will significantly slow NoiseChisel. In verbose mode (without the --quiet option, see Operating mode options) the important steps (along with their extension names) will also be reported.
With this option, NoiseChisel will abort as soon as the two tables are created. This behavior can be disabled with --continueaftercheck.