Description¶
- Classes:
- Aliases:
outlier_detection, outlier_detection_scaled, outlier_detection_stack
Processing multiple datasets together allows for the identification of bad pixels
or cosmic-rays that remain in each of the input images, many times at levels which
were not detectable by the jump step. The outlier_detection
step
implements the following algorithm to identify and flag any remaining cosmic-rays or
other artifacts left over from previous calibrations:
build a stack of input data
all inputs will need to have the same WCS, which is done by
romancal.tweakreg.TweakRegStep
), since outlier detection assumes the same flux for each point on the sky, and variations from one image to the next would indicate a spurious signalif needed, each input will be resampled to a common output WCS
create a median image from the stack of input data
this median operation will ignore any input pixels which have a weight which is too low (<70% max weight)
create “blotted” data from the median image to exactly match each original input dataset
perform a statistical comparison (pixel-by-pixel) between the median blotted data with the original input data to look for pixels with values that are different from the mean value by more than some specified sigma based on the noise model
the noise model used relies on the error array computed by previous calibration steps based on the readnoise and calibration errors
flag the DQ array for the input data for any pixel (or affected neighboring pixels) identified as a statistical outlier.