Describe the process of "detection", or finding objects in individual PanSTARRS exposures, and the quantities associated with a detection.  In PanSTARRS nomenclature, a detection is a source found in a single exposure or a stacked image. Each detection has associated quantities. Detections are combined into "objects" by spatial matching across different exposures and filters. 

The starting point for the PS1 data archive is at the Pan-STARRS1 data archive home page.

Definition - what is a "detection"?

In PanSTARRS lingonomenclature, a detection is a source found in a specific single exposure or a stacked image.  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.:

1. Smooth

1. the image with PSF (or

1. a PSF estimate in the first pass)
2. Smooth

1. the variance with PSF**2
   • To speed these up,

1. • a 1D Gaussian with FWHM matching the PSF

1. • is used.
   • That is much faster

1. • and is only marginally different.
   • If

1. • the difference matters, the image is

1. • of poor quality.

1. Create a significance image by dividing

1. image**2 / variance.
2. Find all peaks above target S/N (squared).

1. Perform a footprint analysis

1. • Generate isophotal footprint outlines (N sigma above sky).

1. • Assign peaks to their containing footprints.

1. • Cull insignificant peaks:

1. • • Cull in descending order of brightness

1. • • .
     • A valid peak must be separated from a brighter peak by a significant valley.
     • As a recent

1. • • improvement: on the second pass, cull on the unsubtracted

Some information from Tonry 2012a (white dwarfs from MDF data) to be placed in this page tree. 

1. • • image

2) Stacking.  The process Tonry describes involves weighting images from different nights by their inverse variance times the inverse PSF area, nearly optimal for point-source detection.  No convolution is used, thus the effective PSF for a point source will have a sharp core due to images obtained in better seeing, and a skirt due to poorer seeing.  He claims that deconvolution of the skirt would be possible, but it was not attempted.

3) Photometric calibration.  Tonry lists a sequence of steps used to bring different images to a common zero point. 

a) obtain positions and fluxes for all stars in each nightly stack using DOPHOT

b) obtain photometric and astrometric offsets for each skycell by pairwise comparing data for all sources. (Rejection?)  Instrumental magnitudes are defined for a 6" box.

c) stack nightly stacks as described above

d) Obtain instrumental magnitudes for the stats in a stack-stack

e) obtain a zero point for the stack-stack by comparison with SDSS-converted-to-PanSTARRS1, 2MASS+stellar locus correction,

pairwise

PSF Photometry

PSF fitting

Photometric zero points

Aperture Photometry

Kron Photometry

image smoothing, sky level, measuring moments, aperture size, iterations

Forced Photometry