Description

Class:: jwst.skymatch.SkymatchStep
Alias:: skymatch

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 doing cosmic-ray rejection and combining multiple images into a mosaic. When running the skymatch step in a matching mode, it compares total signal levels in the overlap regions of a set of input images and computes the signal offsets either for each image or a set/group of images (see Image Groups section below) that will minimize – in a least squares sense – the residuals across the entire set. This comparison is performed directly on the 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. Matching based on total signal level is especially useful for images that are dominated by large, diffuse sources, where it is difficult – if not impossible – to find and measure true sky.

Note that the meaning of “sky background” depends on the chosen sky computation method. When the matching method is used, for example, the reported “sky” value is only the offset in levels between images and does not necessarily include the true total sky level.

Note

Throughout this document the term “sky” is used in a generic sense, referring to any kind of non-source background signal, which may include actual sky, as well as instrumental (e.g. thermal) background, etc.

The step records information in three keywords that are included in the output files:

BKGMETH: records the sky method that was used to compute sky levels
BKGLEVEL: the sky level computed for each image
BKGSUB: a boolean indicating whether or not the sky was subtracted from the output images. Note that by default the step argument “subtract” is set to False, which means that the sky will NOT be subtracted (see the skymatch step arguments for more details).

Both the “BKGSUB” and “BKGLEVEL” keyword values are important information for downstream tasks, such as outlier detection and resampling. Outlier detection will use the BKGLEVEL values to internally equalize the images, which is necessary to prevent false detections due to overall differences in signal levels between images, and the resample step will subtract the BKGLEVEL values from each input image when combining them into a mosaic.

Sky background

For a detailed discussion of JWST background components, please see Rigby et al. “How Dark the Sky: The JWST Backgrounds”, 2023 and “JWST Background Model” section in the JWST User Documentation Here we just note that some components (e.g., in-field zodiacal light) result in reproducible background structures in all detectors when they are exposed simultaneously, while other components (e.g. stray light, thermal emission) can produce varying background from one exposure to the next exposure. The type of background structure that dominates a particular dataset affects the optimal way to group images in the skymatch step.

Image Groups

When computing and matching sky background on a set of input images, a single sky level (or offset, depending on selected skymethod) can be computed either for each input image or for groups of two or more input images.

When background is dominated by zodiacal light, images taken at the same time (e.g., NIRCam images from all short-wave detectors) can be sky matched together; that is, a single background level can be computed and applied to all these images because we can assume that for the next exposure we will get a similar background structure, albeit with an offset level (common to all images in an exposure). Using grouped images with a common background level offers several advantages: more data are used to compute the single sky level, and the background level of images that do not overlap individually with any other images in other exposures can still be adjusted (as long as they belong to a group). This is the default operating mode for the skymatch step.

Identification of images that belong to the same “exposure” and therefore can be grouped together is based on several attributes described in jwst.datamodels.ModelContainer. This grouping is performed automatically in the skymatch step using the jwst.datamodels.ModelContainer.models_grouped property or jwst.datamodels.ModelLibrary.group_indices().

However, when background across different detectors in a single “exposure” (or “group”) is dominated by unpredictable background components, we no longer can use a single background level for all images in a group. In this case, it may be desirable to match image backgrounds independently. This can be achieved either by setting the image_model.meta.group_id attribute to a unique string or integer value for each image, or by adding the group_id attribute to the members of the input ASN table - see ModelContainer for more details.

Note

Group ID (group_id) is used by both tweakreg and skymatch steps and so modifying it for one step will affect the results in another step. If it is desirable to apply different grouping strategies to the tweakreg and skymatch steps, one may need to run each step individually and provide a different ASN as input to each step.

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.

Algorithms

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

The first method, called “local”, essentially is an enhanced version of the original sky subtraction method used in older versions of astrodrizzle. 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 to remove bad pixels from being used in the computations of sky statistics.

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

The “global” method essentially uses the “local” method to first compute a sky value for each image separately, and then assigns the minimum of those results to all images in the collection. Hence after subtraction of the sky values only one image will have a net sky of zero, while the remaining images will have some small positive residual.
The “match” algorithm computes only a correction value for each image, such that, when applied to each image, the mismatch between all pairs of images is minimized, in the least-squares sense. For each pair of images, the sky 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 pair-wise intersection of adjacent images without having a common intersection region (on the sky) in all images.

Note that if the argument “match_down=True”, matching will be done to the image with the lowest sky value, and if “match_down=False” it will be done to the image with the highest value (see skymatch step arguments for full details).
The “global+match” algorithm combines the “global” and “match” methods. It uses the “global” algorithm to find a baseline sky value common to all input images and the “match” algorithm to equalize sky values among images. The direction of matching (to the lowest or highest) is again controlled by the “match_down” argument.

In the “local” and “global” methods, which find sky levels in each image, the calculation of the image statistics takes advantage of sigma clipping to remove contributions from isolated sources. This can work well for accurately determining the true sky level in images that contain semi-large regions of empty sky. The “match” algorithm, on the other hand, compares the total signal levels integrated over regions of overlap in each image pair. This method can produce better results when there are no large empty regions of sky in the images. This method cannot measure the true sky level, but instead provides additive corrections that can be used to equalize the signal between overlapping images.

User-Supplied Sky Values

The skymatch step can also accept user-supplied sky values for each image. This is useful when sky values have been determined based on a custom workflow outside the pipeline. To use this feature, the user must provide a list of sky values matching the number of images (skylist parameter) and set the skymethod parameter to “user”. The skylist must be a two-column whitespace-delimited file with the first column containing the image filenames and the second column containing the sky values. There must be exactly one line per image in the input list.

Examples

To get a better idea of the behavior of these different methods, the tables below show the results for two hypothetical sets of images. The first example is for a set of 6 images that form a 2x3 mosaic, with every image having overlap with its immediate neighbors. The first column of the table gives the actual (fake) sky signal that was imposed in each image, and the subsequent columns show the results computed by each method (i.e. the values of the resulting BKGLEVEL keywords). All results are for the case where the step argument match_down = True, which means matching is done to the image with the lowest sky value. Note that these examples are for the highly simplistic case where each example image contains nothing but the constant sky value. Hence the sky computations are not affected at all by any source content and are therefore able to determine the sky values exactly in each image. Results for real images will of course not be so exact.

Sky	Local	Global	Match	Global+Match
100	100	100	0	100
120	120	100	20	120
105	105	100	5	105
110	110	100	10	110
105	105	100	5	105
115	115	100	15	115

local: finds the sky level of each image independently of the rest.
global: uses the minimum sky level found by “local” and applies it to all images.
match: with “match_down=True” finds the offset needed to match all images to the level of the image with the lowest sky level.
global+match: with “match_down=True” finds the offsets and global value needed to set all images to a sky level of zero. In this trivial example, the results are identical to the “local” method.

The second example is for a set of 7 images, where the first 4 form a 2x2 mosaic, with overlaps, and the second set of 3 images forms another mosaic, with internal overlap, but the 2 mosaics do NOT overlap one another.

Sky	Local	Global	Match	Global+Match
100	100	90	0	86.25
120	120	90	20	106.25
105	105	90	5	91.25
110	110	90	10	96.25
95	95	90	8.75	95
90	90	90	3.75	90
100	100	90	13.75	100

In this case, the “local” method again computes the sky in each image independently of the rest, and the “global” method sets the result for each image to the minimum value returned by “local”. The matching results, however, require some explanation. With “match” only, all of the results give the proper offsets required to equalize the images contained within each mosaic, but the algorithm does not have the information needed to match the two (non-overlapping) mosaics to one another. Similarly, the “global+match” results again provide proper matching within each mosaic, but will leave an overall residual in one of the mosaics.

Limitations and Discussions

As aluded to above, the best sky computation method depends on the nature of the data in the input images. If the input images contain mostly compact, isolated sources, the “local” and “global” algorithms can do a good job at finding the true sky level in each image. If the images contain large, diffuse sources, the “match” algorithm is more appropriate, assuming of course there is sufficient overlap between images from which to compute the matching values. In the event there is not overlap between all of the images, as illustrated in the second example above, the “match” method can still provide useful results for matching the levels within each non-contigous region covered by the images, but will not provide a good overall sky level across all of the images. In these situations it is more appropriate to either process the non-contiguous groups independently of one another or use the “local” or “global” methods to compute the sky separately in each image. The latter option will of course only work well if the images are not domimated by extended, diffuse sources.

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 the sky values in the overlap regions of each image pair. The quality of sky matching will obviously depend on how well these sky values can be estimated. True background may not be present at all in some images, in which case the computed “sky” may be the surface brightness of a large galaxy, nebula, etc.

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

Because sky computation is performed on flat-fielded but not distortion corrected images, it is important to keep in mind that flat-fielding is performed to obtain correct surface brightnesses. Because the surface brightness of a pixel containing a point-like source will change inversely with a change to the pixel area, it is advisable to mask point-like sources through user-supplied mask files. Values different from zero in user-supplied masks indicate good data pixels. Alternatively, one can use the upper parameter to exclude the use of pixels containing bright objects when performing 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 (see skymatch step arguments).