No measurement on a real dataset can be perfect: you can only reach a certain level/limit of accuracy and a meaningful (scientific) analysis requires an understanding of these limits. Different datasets have different noise properties and different detection methods (one method/algorithm/software that is run with a different set of parameters is considered as a different detection method) will have different abilities to detect or measure certain kinds of signal (astronomical objects) and their properties in the dataset. Hence, quantifying the detection and measurement limitations with a particular dataset and analysis tool is the most crucial/critical aspect of any high-level analysis. In two separate tutorials, we have touched upon some of these points. So to see the discussions below in action (on real data), see Measuring the dataset limits and Image surface brightness limit.
Here, we will review some of the most commonly used methods to quantify the limits in astronomical data analysis and how MakeCatalog makes it easy to measure them. Depending on the higher-level analysis, there are more tests that must be done, but these are relatively low-level and usually necessary in most cases. In astronomy, it is common to use the magnitude (a unit-less scale) and physical units, see Brightness, Flux, Magnitude and Surface brightness. Therefore the measurements discussed here are commonly used in units of magnitudes.