DecorrelateALKernelTask

Pipe-task that removes the neighboring-pixel covariance in an image difference that are added when the template image is convolved with the Alard-Lupton PSF matching kernel.

Description

The image differencing pipeline task PsfMatchTask and PsfMatchConfigAL uses the Alard and Lupton (1998) method for matching the PSFs of the template and science exposures prior to subtraction. The Alard-Lupton method identifies a matching kernel, which is then (typically) convolved with the template image to perform PSF matching. This convolution has the effect of adding covariance between neighboring pixels in the template image, which is then added to the image difference by subtraction.

The pixel covariance may be corrected by whitening the noise of the image difference. This task performs such a decorrelation by computing a decorrelation kernel (based upon the A&L matching kernel and variances in the template and science images) and convolving the image difference with it. This process is described in detail in [DMTN-021](http://dmtn-021.lsst.io).

This task has no standalone example, however it is applied as a subtask of ImageDifferenceTask.

Python API summary

from lsst.ip.diffim.imageDecorrelation import DecorrelateALKernelTask

classDecorrelateALKernelTask(*args, **kwargs)

Decorrelate the effect of convolution by Alard-Lupton matching kernel in image difference...

attributeconfig

Access configuration fields and retargetable subtasks.

methodrun(scienceExposure, templateExposure, subtractedExposure, psfMatchingKernel, preConvKernel=None, xcen=None, ycen=None, svar=None, tvar=None, templateMatched=True, preConvMode=False, **kwargs)

Perform decorrelation of an image difference or of a score difference exposure...

See also

See the DecorrelateALKernelTask API reference for complete details.

Retargetable subtasks

No subtasks.

Configuration fields

completeVarPlanePropagation

Default

False

Field type

bool Field

Compute the full effect of the decorrelated matching kernel on the variance plane. Otherwise use a model weighed sum of the input variances.

ignoreMaskPlanes

Default

('INTRP', 'EDGE', 'DETECTED', 'SAT', 'CR', 'BAD', 'NO_DATA', 'DETECTED_NEGATIVE')

Field type

str ListField

Mask planes to ignore for sigma-clipped statistics