At the lowest level, a dataset (for example an image) is just a collection of values, placed after each other in any number of dimensions (for example an image is a 2D dataset). Each data-element (pixel) just has two properties: its position (relative to the rest) and its value. In higher-level analysis, an entire dataset (an image for example) is rarely treated as a singular entity133. You usually want to know/measure the properties of the (separate) scientifically interesting targets that are embedded in it. For example the magnitudes, positions and elliptical properties of the galaxies that are in the image.
MakeCatalog is Gnuastro’s program for localized measurements over a dataset. The role of MakeCatalog in a scientific analysis and the benefits of this model of data analysis (were detection/segmentation is separated from measurement) is discussed in Akhlaghi . We strongly recommend reading this short paper for a better understanding of this methodology thus effective usage of MakeCatalog, in combination with the other Gnuastro’s programs. However, that paper cannot undergo any more change, so this manual is the definitive guide.
It is important to define your regions of interest before running MakeCatalog. MakeCatalog is specialized in doing measurements accurately and efficiently. Therefore MakeCatalog will not do detection, segmentation, or defining apertures on requested positions in your dataset. Following Gnuastro’s modularity principle, There are separate and highly specialized and customizable programs in Gnuastro for these other jobs:
These programs will/can return labeled dataset(s) to be fed into MakeCatalog. The labeled dataset must have the same size/dimensions as the input, but only with integer valued pixels that have the label/counter for the feature the pixel belongs to.
These labels are then directly used to make the necessary measurements. For example the flux weighted average position of all the pixels with a label of 42 will be considered as the central position134 of the 42nd row of the output catalog. Similarly, the sum of all these pixels will be the 42nd row in the brightness column and etc. Pixels with labels equal to or smaller than zero will be ignored by MakeCatalog. In other words, the number of rows in MakeCatalog’s output is already known before running it.
Before getting into the details of running MakeCatalog (in Invoking MakeCatalog, we’ll start with a discussion on the basics of its approach to separating detection from measurements in Detection and catalog production. A very important factor in any measurement is understanding its validity range, or limits. Therefore in Quantifying measurement limits, we’ll discuss how to estimate the reliability of the detection and basic measurements. This section will continue with a derivation of elliptical parameters from the labeled datasets in Measuring elliptical parameters. For those who feel MakeCatalog’s existing measurements/columns aren’t enough and would like to add further measurements, in Adding new columns to MakeCatalog, a checklist of steps is provided for readily adding your own new measurements/columns.
|• Detection and catalog production:||Discussing why/how to treat these separately|
|• Quantifying measurement limits:||For comparing different catalogs.|
|• Measuring elliptical parameters:||Estimating elliptical parameters.|
|• Adding new columns to MakeCatalog:||How to add new columns.|
|• Invoking astmkcatalog:||Options and arguments to MakeCatalog.|
You can derive the over-all properties of a complete dataset (1D table column, 2D image, or 3D data-cube) treated as a single entity with Gnuastro’s Statistics program (see Statistics).
See Measuring elliptical parameters for a discussion on this and the derivation of positional parameters.