PS1 took approximately 370,000 images from 2010 to 2015. These images are detrended, astrometrically calibrated, resampled (warped) onto a standard sky coordinate grid, stacked, and differenced. The different types of images are described below. More details about image filenames and data formats are included in the description of the PS1 archive image cutout interface. 

Raw Images

After an exposure is taken at the summit, the raw image files of the 60 OTA CCDs are corrected for persistence issues and then handed over to the IPP processing for further analysis. The STScI PS1 Archive does not provide these images.

Camera Images

The camera images are created by detrending the raw images (e.g., masking, bias subtraction, flat fielding), and applying an astrometric and photometric calibration. The STScI PS1 Archive does not provide these images.

Image Artifacts and Anomalies

Warp Images (DR1)

Stack Images (DR1)

Difference Images

Difference images are created by subtracting a warp from another image, in general the stack, after matching the PSFs and normalization using a spatially varying kernel. All static objects like galaxies and constant stars are subtracted out, and only the excess flux from the different epochs is left. Even though the differences images are not stored on disk, they can be created, and it is planned to make them accessible through the STScI PS1 interface in a later release.

Mask Images

All images, warps and stacks, have Pixel Flags set for each individual pixels. This information is saved in a mask image, which in general has the suffix .mask.fits(.fz) and mk.fits(.fz) for warp and stack images, respectively.

Weight Images

Weight images are variance maps. For single epoch warps, these variance contains the readnoise, Poisson noise, IfA: FILL with other contributions. For stacks, the noise is propagated from the individual input warps. These weight images can be used for estimates of the uncertainties in the photometry. We note that the deprojection of the chip images into the warps correlates the pixels, introducing covariance. Therefore the derived uncertainties might underestimate the true noise.