Description

Class:

jwst.badpix_selfcal.BadpixSelfcalStep

Alias:

badpix_selfcal

Overview

The badpix_selfcal step flags bad pixels in the input data using a self-calibration technique based on median filtering along the spectral axis. When additional exposures are available, those are used in combination with the science exposure to identify bad pixels; when unavailable, the step will be skipped with a warning unless the force_single parameter is set True. In that case, the science data alone is used as its own “background”. This correction is applied to IFUImageModel data directly after the assign_wcs correction has been applied in the calwebb_spec2 pipeline.

Input details

The input data must be in the form of a IFUImageModel or a ModelContainer containing exactly one science exposure and any number of additional exposures. A fits or association file that can be read into one of these data models is also acceptable. Any exposure with the metadata attribute asn.exptype set to background or selfcal will be used in conjunction with the science exposure to construct the combined background image.

Algorithm

The algorithm relies on the assumption that bad pixels are outliers in the data along the spectral axis. The algorithm proceeds as follows:

  • A combined background image is created. If additional (selfcal or background) exposures are available, the pixelwise minimum of all background, selfcal, and science exposures is taken. If no additional exposures are available, the science data itself is passed in without modification, serving as the “background image” for the rest of the procedure, i.e., true self-calibration.

  • For MIRI MRS, any residual pedestal in the effective dark current is subtracted from each exposure using the unilluminated region of the detector between spectral channels prior to performing the pixelwise minimum combination.

  • The combined background image is median-filtered, ignoring NaNs, along the spectral axis with a user-specified kernel size. The default kernel size is 15 pixels.

  • The difference between the original background image and the median-filtered background image is taken. The highest- and lowest-flux pixels in this difference image are flagged as bad pixels. The default fraction of pixels to flag is 0.1% of the total number of pixels on each of the high-flux and low-flux ends of the distribution. These fractions can be adjusted using the flagfrac_lower and flagfrac_upper parameters for the low- and high-flux ends of the distribution, respectively. The total fraction of flagged pixels is thus flagfrac_lower + flagfrac_upper.

  • The bad pixels are flagged in the input data by setting the DQ flag to “OTHER_BAD_PIXEL” and “DO_NOT_USE”.

  • The bad pixels are also flagged in each exposure with asn.exptype equal to background, if available.

Output product

The output is a new copy of the input IFUImageModel, with the bad pixels flagged. If the entire calwebb_spec2 pipeline is run, the background exposures passed into the background step will include the flags from the badpix_selfcal step.