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|This page provides detailed descriptions of various morphological
outputs of the photometry pipelines. We also provide discussion of
some methodology; for details of the Photo pipeline processing please
visit the Photo pipeline
page. Other photometric outputs, specifically the various
magnitudes, are described on the photometry
The frames pipeline also provides several characterizations of the shape and morphology of an object.
In particular, Lupton et al. (2001a) show that the following simple cut works at the 95% confidence level for our data to r=21 and even somewhat fainter:
psfMag - (dev_L>exp_L)?deVMag:expMag)>0.145
If satisfied, type is set to GALAXY for that band; otherwise, type is set to STAR . The global type objc_type is set according to the same criterion, applied to the summed fluxes from all bands in which the object is detected.
Experimentation has shown that simple variants on this scheme, such as defining galaxies as those objects classified as such in any two of the three high signal-to-noise ratio bands (namely, g, r, and i), work better in some circumstances. This scheme occasionally fails to distinguish pairs of stars with separation small enough (<2") that the deblender does not split them; it also occasionally classifies Seyfert galaxies with particularly bright nuclei as stars.
Further information to refine the star-galaxy separation further may be used, depending on scientific application. For example, Scranton et al. (2001) advocate applying a Bayesian prior to the above difference between the PSF and exponential magnitudes, depending on seeing and using prior knowledge about the counts of galaxies and stars with magnitude.
When converting the profMean values to a local surface
brightness, it is not the best approach to assign the mean
surface brightness to some radius within the annulus and then linearly
interpolate between radial bins. Do not use smoothing
splines, as they will not go through the points in the cumulative
profile and thus (obviously) will not conserve flux. What frames
does, e.g., in determining the Petrosian ratio, is to fit a taut spline to the
cumulative profile and then differentiate that spline fit,
after transforming both the radii and cumulative profiles with asinh
functions. We recommend doing the same here.
Surface Brightness & Concentration Index
It turns out that the ratio of petroR50 to petroR90, the so-called "inverse concentration index", is correlated with morphology (Shimasaku et al. 2001, Strateva et al. 2001). Galaxies with a de Vaucouleurs profile have an inverse concentration index of around 0.3; exponential galaxies have an inverse concentration index of around 0.43. Thus, this parameter can be used as a simple morphological classifier.
An important caveat when using these quantities is that they are not corrected for seeing. This causes the surface brightness to be underestimated, and the inverse concentration index to be overestimated, for objects of size comparable to the PSF. The amplitudes of these effects, however, are not yet well characterized.
Model Fit Likelihoods and Parameters
and similarly for f(exp_L) and f(star_L). A fractional likelihood greater than 0.5 for any of these three profiles is generally a good threshold for object classification. This works well in the range 18<r<21.5; at the bright end, the likelihoods have a tendency to underflow to zero, which makes them less useful. In particular, star_L is often zero for bright stars. For future data releases we will incorporate improvements to the model fits to give more meaningful results at the bright end.
The first method measures flux-weighted second moments,