One of the most important aspects of a dataset is its reference value: the value of the dataset where there is no signal. Without knowing, and thus removing the effect of, this value it is impossible to compare the derived results of many high-level analyses over the dataset with other datasets (in the attempt to associate our results with the “real” world).
In astronomy, this reference value is known as the “Sky” value: the value that noise fluctuates around: where there is no signal from detectable objects or artifacts (for example galaxies, stars, planets or comets, star spikes or internal optical ghost). Depending on the dataset, the Sky value maybe a fixed value over the whole dataset, or it may vary based on location. For an example of the latter case, see Figure 11 in Akhlaghi and Ichikawa (2015).
Because of the significance of the Sky value in astronomical data analysis, we have devoted this subsection to it for a thorough review. We start with a thorough discussion on its definition (Sky value definition). In the astronomical literature, researchers use a variety of methods to estimate the Sky value, so in Sky value misconceptions) we review those and discuss their biases. From the definition of the Sky value, the most accurate way to estimate the Sky value is to run a detection algorithm (for example NoiseChisel) over the dataset and use the undetected pixels. However, there is also a more crude method that maybe useful when good direct detection is not initially possible (for example due to too many cosmic rays in a shallow image). A more crude (but simpler method) that is usable in such situations is discussed in Quantifying signal in a tile.
|• Sky value definition||Definition of the Sky/reference value.|
|• Sky value misconceptions||Wrong methods to estimate the Sky value.|
|• Quantifying signal in a tile||Method to estimate the presence of signal.|