# Description¶

## Overview¶

The skymatch step can be used to compute sky values in a collection of input images that contain both sky and source signal. The sky values can be computed for each image separately or in a way that matches the sky levels amongst the collection of images so as to minimize their differences. This operation is typically applied before combining multiple images into a mosaic. When running the skymatch step in a matching mode, it compares total signal levels in the overlap regions (instead of doing this comparison on a per-pixel basis) of a set of input images and computes the signal offsets for each image that will minimize the residuals across the entire set in the least squares sence. This comparison is performed directly on input images without resampling them onto a common grid. The overlap regions are computed directly on the sky (celestial sphere) for each pair of input images. By default the sky value computed for each image is recorded, but not actually subtracted from the images. Also note that the meaning of “sky background” depends on the chosen sky computation method.

## Assumptions¶

When matching sky background, the code needs to compute bounding polygon intersections in world coordinates. The input images, therefore, need to have a valid WCS, generated by the assign_wcs step.

## Algorithm¶

The skymatch step provides several methods for constant sky background value computations.

The first method, called “localmin”, essentially is an enhanced version of the original sky subtraction method used in older astrodrizzle versions. This method simply computes the mean/median/mode/etc. value of the “sky” separately in each input image. This method was upgraded to be able to use DQ flags and user-supplied masks to remove bad pixels from being used for sky statistics computations. Values different from zero in user-supplied masks indicate good data pixels.

In addition to the classical “localmin” method, two other methods have been introduced: “globalmin” and “match”, as well as a combination of the two – “globalmin+match”.

• The “globalmin” method computes the minimum sky value across all input images. The resulting single sky value is then considered to be the background in all input images.

• The “match” algorithm computes constant (within an image) value corrections to be applied to each input image such that the mismatch in computed backgrounds between all pairs of images is minimized in the least squares sense. For each pair of images, the background mismatch is computed only in the regions in which the two images overlap on the sky.

This makes the “match” algorithm particularly useful for equalizing sky values in large mosaics in which one may have only (at least) pair-wise intersection of images without having a common intersection region (on the sky) in all images.

• The “globalmin+match” algorithm combines the “match” and “globalmin” methods. It uses the “globalmin” algorithm to find a baseline sky value common to all input images and the “match” algorithm to equalize sky values among images.

In methods that find sky background levels in each image (“localmin”) or a single level for all images (“globalmin”), image statistics are usually computed using sigma clipping. If the input images contain vast swaths of empty sky, then the sigma clipping algorithm should be able to automatically exclude (clip) contributions from bright compact sources. In this case the measured sky background is the measured signal level from the empty sky. On the other hand, the “match” method compares the total signal levels integrated over those regions in the images that correspond to common (overlap) regions on the celestial sphere for both images being compared (comparison is pair-wise). This method is often used when there are no large empty sky regions in the images, such as when a large nebula occupies most of the view. This method cannot measure true background, but rather additive corrections that need to be applied to the input images so that the total signal from the same part of the sky is equal in all images.

## Step Arguments¶

The skymatch step has the following optional arguments:

General sky matching parameters:

• skymethod (str): The sky computation algorithm to be used. Allowed values: {local, global, match, global+match} (Default = global+match)

• match_down (boolean): Specifies whether the sky differences should be subtracted from images with higher sky values (match_down = True) in order to match the image with the lowest sky or sky differences should be added to the images with lower sky values to match the sky of the image with the highest sky value (match_down = False). (Default = True)

Note

This setting applies only when skymethod is either match or global+match.

• subtract (boolean): Specifies whether the computed sky background values are to be subtracted from the images. (Default = False)

Image bounding polygon parameters:

• stepsize (int): Spacing between vertices of the images bounding polygon. Default value of None creates bounding polygons with four vertices corresponding to the corners of the image.

Sky statistics parameters:

• skystat (str): Statistic to be used for sky background value computations. Supported values are: ‘mean’, ‘mode’, ‘midpt’, and ‘median’. (Default = ‘mode’)

• dqbits (str): The DQ bit values from the input images’ DQ arrays that should be considered “good” when building masks for sky computations. See DQ flag Parameter Specification for details. (Default=’0’)

• lower (float): An optional value indicating the lower limit of usable pixel values for computing the sky. This value should be specified in the units of the input images. (Default = None)

• upper (float): An optional value indicating the upper limit of usable pixel values for computing the sky. This value should be specified in the units of the input images. (Default = None)

• nclip (int): A non-negative number of clipping iterations to use when computing the sky value. (Default = 5)

• lsig (float): Lower clipping limit, in sigma, used when computing the sky value. (Default = 4.0)

• usig (float): Upper clipping limit, in sigma, used when computing the sky value. (Default = 4.0)

• binwidth (float): Bin width, in sigma, used to sample the distribution of pixel values in order to compute the sky background using statistics that require binning such as mode and midpt. (Default = 0.1)

## Limitations and Discussions¶

The primary reason for introducing the skymatch algorithm was to try to equalize the sky in large mosaics in which computation of the absolute sky is difficult, due to the presence of large diffuse sources in the image. As discussed above, the skymatch step accomplishes this by comparing sky values in input images in their overlap regions (that is common to a pair of images). Quite obviously the quality of sky matching will depend on how well these sky values can be estimated. In some images true background may not be present at all and the measured “sky” may be the surface brightness of a large galaxy, nebula, etc.

Here is a brief list of possible limitations/factors that can affect the outcome of the matching (sky subtraction in general) algorithm:

• Because sky subtraction is performed on flat-fielded but not distortion corrected images, it is important to keep in mind that flat-fielding is performed to obtain uniform surface brightness and not flux. This distinction is important for images that have not been distortion corrected. As a consequence, it is advisable that point-like sources be masked through the user-supplied mask files. Values different from zero in user-supplied masks indicate good data pixels. Alternatively, one can use the upper parameter to limit the use of pixels containing bright objects in the sky computations.

• The input images may contain cosmic rays. This algorithm does not perform CR cleaning. A possible way of minimizing the effect of the cosmic rays on sky computations is to use clipping (nclip > 0) and/or set the upper parameter to a value larger than most of the sky background (or extended sources) but lower than the values of most CR-affected pixels.

• In general, clipping is a good way of eliminating bad pixels: pixels affected by CR, hot/dead pixels, etc. However, for images with complicated backgrounds (extended galaxies, nebulae, etc.), affected by CR and noise, the clipping process may mask different pixels in different images. If variations in the background are too strong, clipping may converge to different sky values in different images even when factoring in the true difference in the sky background between the two images.

• In general images can have different true background values (we could measure it if images were not affected by large diffuse sources). However, arguments such as lower and upper will apply to all images regardless of the intrinsic differences in sky levels.

# Reference Files¶

This step does not require any reference files.