Description
- Class:
- Alias:
jump
This step finds and flags outliers (usually caused by cosmic-ray hits) in each pixel of an “up the ramp” IR exposure.
Assumptions
We assume that the saturation step has already been applied to the input exposure, so that saturated ramp groups are appropriately flagged in the input GROUPDQ array. We also assume that steps such as reference pixel correction and non-linearity correction have been applied, so that the input data ramps do not have any non-linearities or noise above the modeled Poisson and read noise due to instrumental effects. The absence of any of these preceding corrections or the presence of residual non-linearities and noise can lead to false detection of jumps in the ramps, due to departure from linearity.
The jump step will automatically skip execution if the input data contain fewer
than 3 groups per integration, because the baseline algorithm requires at least
two first differences to work.
Note that the core algorithms for this step are called from the external package
stcal, an STScI effort to unify common calibration processing algorithms
for use by multiple observatories.
Algorithm
Large Events (Snowballs and Showers)
All the detectors on JWST are affected by large cosmic ray events. While these events, in general, affect a large number of pixels, the more distinguishing characteristic is that they are surrounded by a halo of pixels that have a low level of excess counts. These excess counts are, in general, below the detection threshold of normal cosmic rays.
To constrain the effect of this halo, the jump step will fit ellipses or circles that
enclose the large events and expand the ellipses and circles by the input
expand_factor and mark them as jump (see jump step arguments
for details).
The two different types of JWST detectors respond differently. The large events in the near-infrared detectors are almost always circles with a central region that is saturated. The saturated core allows the search for smaller events without false positives. The mid-IR (MIRI) detectors do not, in general, have a saturated center and are only rarely circular. Thus, we fit the minimum enclosing ellipse and do not require that there are saturated pixels within the ellipse.
Multiprocessing
This step has the option of running in multiprocessing mode. In that mode it will
split the input data cube into a number of row slices based on the number of available
cores on the host computer and the value of the max_cores input parameter. By
default the step runs on a single processor. At the other extreme, if max_cores is
set to “all”, it will use all available cores (real and virtual). Testing has shown
a reduction in the elapsed time for the step proportional to the number of real
cores used. Using the virtual cores also reduces the elapsed time, but at a slightly
lower rate than the real cores.
If multiprocessing is requested, the input cube will be divided into a number of slices in the row dimension (with the last slice being slightly larger, if needed), and sent for processing in parallel. In the event the number of cores (and hence slices) selected exceeds the number of available image rows, the number of slices will be reduced to match the number of rows. After all the slices have finished processing, the output GROUPDQ cube - containing the DQ flags for detected jumps - is reassembled from the slices.
Subarrays
Full-frame reference files can be used for all science exposures even if the science exposure was taken in a subarray mode. If so, subarrays will be extracted from the reference file data to match the science exposure. Alternatively, subarray-specific reference files, which match the science exposure, may be used.