Morphology defined as a way to quantify the “shape” of an object in your input image. This includes both the position and value of the pixels within your input labels. There are many ways to define the morphology of an object. In this section, we will review the available non-parametric measures of morphology. By non-parametric, we mean that no functional shape is assumed for the measurement.
In Morphology measurements (elliptical) you can see some parametric elliptical measurements (which are only valid when the object is actually an ellipse).
[Objects] The number of clumps in this object.
The raw area (number of pixels/voxels) in any clump or object independent of what pixel it lies over (if it is NaN/blank or unused for example).
The used (non-blank in values image) area of the labeled region in units of arc-seconds squared. This column is just the value of the --area column, multiplied by the area of each pixel in the input image (in units of arcsec^2). Similar to the --ra or --dec columns, for this option to work, the objects extension used has to have a WCS structure.
The number of pixels that are equal to the minimum value of the labeled region (clump or object).
The number of pixels that are equal to the maximum value of the labeled region (clump or object).
Similar to --area, when the clump or object is projected onto the first two dimensions. This is only available for 3-dimensional datasets. When working with Integral Field Unit (IFU) datasets, this projection onto the first two dimensions would be a narrow-band image.
The full width at half maximum (in units of pixels, along the semi-major axis) of the labeled region (object or clump). The maximum value is estimated from the mean of the top-three pixels with the highest values, see the description under --maximum. The number of pixels that have half the value of that maximum are then found (value in the --half-max-area column) and a radius is estimated from the area. See the description under --half-sum-radius for more on converting area to radius along major axis.
Because of its non-parametric nature, this column is most reliable on clumps and should only be used in objects with great caution. This is because objects can have more than one clump (peak with true signal) and multiple peaks are not treated separately in objects, so the result of this column will be biased.
Also, because of its non-parametric nature, this FWHM it does not account for the PSF, and it will be strongly affected by noise if the object is faint/diffuse So when half the maximum value (which can be requested using the --maximum column) is too close to the local noise level (which can be requested using the --sky-std column), the value returned in this column is meaningless (its just noise peaks which are randomly distributed over the area). You can therefore use the --maximum and --sky-std columns to remove, or flag, unreliable FWHMs. For example, if a labeled region’s maximum is less than 2 times the sky standard deviation, the value will certainly be unreliable (half of that is \(1\sigma\)!). For a more reliable value, this fraction should be around 4 (so half the maximum is 2\(\sigma\)).
The number of pixels with values larger than half the maximum flux within the labeled region. This option is used to estimate --fwhm, so please read the notes there for the caveats and necessary precautions.
The radius of region containing half the maximum flux within the labeled region. This is just half the value reported by --fwhm.
The sum of the pixel values containing half the maximum flux within the labeled region (or those that are counted in --halfmaxarea). This option uses the pixels within --fwhm, so please read the notes there for the caveats and necessary precautions.
The number of pixels that contain half the object or clump’s total sum of pixels (half the value in the --sum column). To count this area, all the non-blank values associated with the given label (object or clump) will be sorted and summed in order (starting from the maximum), until the sum becomes larger than half the total sum of the label’s pixels.
This option is thus good for clumps (which are defined to have a single peak in their morphology), but for objects you should be careful: if the object includes multiple peaks/clumps at roughly the same level, then the area reported by this option will be distributed over all the peaks.
Radius (in units of pixels) derived from the area that contains half the total sum of the label’s pixels (value reported by --halfsumarea). If the area is \(A_h\) and the axis ratio is \(q\), then the value returned in this column is \(\sqrt{A_h/({\pi}q)}\). This option is a good measure of the concentration of the observed (after PSF convolution and noisy) object or clump, But as described below it underestimates the effective radius. Also, it should be used in caution with objects that may have multiple clumps. It is most reliable with clumps or objects that have one or zero clumps, see the note under --halfsumarea.
Recall that in general, for an ellipse with semi-major axis \(a\), semi-minor axis \(b\), and axis ratio \(q=b/a\) the area (\(A\)) is \(A={\pi}ab={\pi}qa^2\). For a circle (where \(q=1\)), this simplifies to the familiar \(A={\pi}a^2\).
This option should not be confused with the effective radius for Sérsic profiles, commonly written as \(r_e\). For more on the Sérsic profile and \(r_e\), please see Galaxies. Therefore, when \(r_e\) is meaningful for the target (the target is elliptically symmetric and can be parameterized as a Sérsic profile), \(r_e\) should be derived from fitting the profile with a Sérsic function which has been convolved with the PSF. But from the equation above, you see that this radius is derived from the raw image’s labeled values (after convolution, with no parametric profile), so this column’s value will generally be (much) smaller than \(r_e\), depending on the PSF, depth of the dataset, the morphology, or if a fraction of the profile falls on the edge of the image.
In other words, this option can only be interpreted as an effective radius if there is no noise and no PSF and the profile within the image extends to infinity (or a very large multiple of the effective radius) and it not near the edge of the image.
Number of pixels brighter than the given fraction(s) of the maximum pixel value. For the maximum value, see the description of --maximum column. The fraction(s) are given through the --frac-max option (that can take two values) and is described in MakeCatalog inputs and basic settings. Recall that in --halfmaxarea, the fraction is fixed to 0.5. Hence, added with these two columns, you can sample three parts of the profile area.
Sum of pixels brighter than the given fraction(s) of the maximum pixel value. For the maximum value, see the description of --maximum column below. The fraction(s) are given through the --frac-max option (that can take two values) and is described in MakeCatalog inputs and basic settings. Recall that in --halfmaxsum, the fraction is fixed to 0.5. Hence, added with these two columns, you can sample three parts of the profile’s sum of pixels.
Radius (in units of pixels) derived from the area that contains the given fractions of the maximum valued pixel(s) of the label’s pixels (value reported by --frac-max1-area or --frac-max2-area). For the maximum value, see the description of --maximum column below. The fractions are given through the --frac-max option (that can take two values) and is described in MakeCatalog inputs and basic settings. Recall that in --fwhm, the fraction is fixed to 0.5. Hence, added with these two columns, you can sample three parts of the profile’s radius.
[Objects] The total area of all the clumps in this object.
The area (number of pixels) used in the flux weighted position calculations.
The area of all the pixels labeled with an object or clump. Note that unlike --area, pixel values are completely ignored in this column. For example, if a pixel value is blank, it will not be counted in --area, but will be counted here.
Similar to --geo-area, when the clump or object is projected onto the first two dimensions. This is only available for 3-dimensional datasets. When working with Integral Field Unit (IFU) datasets, this projection onto the first two dimensions would be a narrow-band image.
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GNU Astronomy Utilities 0.22 manual, February 2024.