Describes the process of "detection", or finding objects in individual PanSTARRS exposures, and the quantities associated with a detection. Detections are combined into "objects" by spatial matching across different exposures and filters.
Contents
The starting point for the PS1 data archive is at the Pan-STARRS1 data archive home page.
Definition - what is a "detection"?
In PanSTARRS nomenclature, a detection is a source found in a single exposure. Detections are matched across exposures to define objects.
Detections are identified through a standard peak-finding algorithm. The image is convolved with an approximation of the PSF and then divided by a smoothed version of the variance image to define the significance image. Peaks are defined as locations where the significance image exceeds a target threshold, representing the square of the desired signal-to-noise ratio. Peaks are then ordered in decreasing significance, and peaks are retained only if a significant valley separates them from brighter nearby peaks.
The process of identifying detections is complex and involves multiple steps:
- Smooth the image with PSF (or a PSF estimate in the first pass)
- Smooth the variance with PSF**2
- To speed these up, a 1D Gaussian with FWHM matching the PSF is used.
- That is much faster and is only marginally different.
- If the difference matters, the image is of poor quality.
- Create a significance image by dividing image**2 / variance.
- Find all peaks above target S/N (squared).
- Perform a footprint analysis
- Generate isophotal footprint outlines (N sigma above sky).
- Assign peaks to their containing footprints.
- Cull insignificant peaks:
- Cull in descending order of brightness.
- A valid peak must be separated from a brighter peak by a significant valley.
- As a recent improvement: on the second pass, cull on the unsubtracted image
