DecorrelateALKernelTask

class lsst.ip.diffim.DecorrelateALKernelTask(*args, **kwargs)

Bases: lsst.pipe.base.Task

! @anchor DecorrelateALKernelTask

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

@section ip_diffim_imageDecorrelation_DecorrelateALKernelTask_Contents Contents

• @ref ip_diffim_imageDecorrelation_DecorrelateALKernelTask_Purpose
• @ref ip_diffim_imageDecorrelation_DecorrelateALKernelTask_Config
• @ref ip_diffim_imageDecorrelation_DecorrelateALKernelTask_Run
• @ref ip_diffim_imageDecorrelation_DecorrelateALKernelTask_Debug
• @ref ip_diffim_imDecorr_DecorrALKernelTask_Example

@section ip_diffim_imageDecorrelation_DecorrelateALKernelTask_Purpose Description

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.

The image differencing pipeline task @link ip.diffim.psfMatch.PsfMatchTask PSFMatchTask@endlink and @link ip.diffim.psfMatch.PsfMatchConfigAL PSFMatchConfigAL@endlink 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).

@section ip_diffim_imageDecorrelation_DecorrelateALKernelTask_Initialize Task initialization

@copydoc __init__

@section ip_diffim_imageDecorrelation_DecorrelateALKernelTask_Run Invoking the Task

@copydoc run

@section ip_diffim_imageDecorrelation_DecorrelateALKernelTask_Config Configuration parameters

See @ref DecorrelateALKernelConfig

@section ip_diffim_imageDecorrelation_DecorrelateALKernelTask_Debug Debug variables

This task has no debug variables

@section ip_diffim_imDecorr_DecorrALKernelTask_Example Example of using DecorrelateALKernelTask

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

Methods Summary

computeCorrectedDiffimPsf(kappa, psf[, …])	! Compute the (decorrelated) difference image’s new PSF.
computeVarianceMean(exposure)
run(exposure, templateExposure, …[, …])	! Perform decorrelation of an image difference exposure.

Methods Documentation

static computeCorrectedDiffimPsf(kappa, psf, svar=0.04, tvar=0.04)

! Compute the (decorrelated) difference image’s new PSF. new_psf = psf(k) * sqrt((svar + tvar) / (svar + tvar * kappa_ft(k)**2))

@param kappa A matching kernel array derived from Alard & Lupton PSF matching @param psf The uncorrected psf array of the science image (and also of the diffim) @param svar Average variance of science image used for PSF matching @param tvar Average variance of template image used for PSF matching @return a 2-d numpy.array containing the new PSF

computeVarianceMean(exposure)

run(exposure, templateExposure, subtractedExposure, psfMatchingKernel, preConvKernel=None, xcen=None, ycen=None, svar=None, tvar=None)

! Perform decorrelation of an image difference exposure.

Decorrelates the diffim due to the convolution of the templateExposure with the A&L PSF matching kernel. Currently can accept a spatially varying matching kernel but in this case it simply uses a static kernel from the center of the exposure. The decorrelation is described in [DMTN-021, Equation 1](http://dmtn-021.lsst.io/#equation-1), where exposure is I_1; templateExposure is I_2; subtractedExposure is D(k); psfMatchingKernel is kappa; and svar and tvar are their respective variances (see below).

@param[in] exposure the science afwImage.Exposure used for PSF matching @param[in] templateExposure the template afwImage.Exposure used for PSF matching @param[in] subtractedExposure the subtracted exposure produced by ip_diffim.ImagePsfMatchTask.subtractExposures() @param[in] psfMatchingKernel an (optionally spatially-varying) PSF matching kernel produced by ip_diffim.ImagePsfMatchTask.subtractExposures() @param[in] preConvKernel if not None, then the exposure was pre-convolved with this kernel @param[in] xcen X-pixel coordinate to use for computing constant matching kernel to use If None (default), then use the center of the image. @param[in] ycen Y-pixel coordinate to use for computing constant matching kernel to use If None (default), then use the center of the image. @param[in] svar image variance for science image If None (default) then compute the variance over the entire input science image. @param[in] tvar image variance for template image If None (default) then compute the variance over the entire input template image.

@return a pipeBase.Struct containing:

• correctedExposure: the decorrelated diffim
• correctionKernel: the decorrelation correction kernel (which may be ignored)

@note The subtractedExposure is NOT updated @note The returned correctedExposure has an updated PSF as well. @note Here we currently convert a spatially-varying matching kernel into a constant kernel, just by computing it at the center of the image (tickets DM-6243, DM-6244). @note We are also using a constant accross-the-image measure of sigma (sqrt(variance)) to compute the decorrelation kernel. @note Still TBD (ticket DM-6580): understand whether the convolution is correctly modifying the variance plane of the new subtractedExposure.