Description¶
- Class:
- Alias:
charge_migration
Overview¶
This step corrects for an artifact seen in undersampled NIRISS images that may depress flux in resampled images. The artifact is seen in dithered images where the star is centered in a pixel. When the peak pixels of such stars approach the saturation level, they suffer from significant charge migration: the spilling of charge from a saturated pixel into its neighboring pixels. This charge migration causes group-to-group differences to decrease significantly once the signal level is greater than ~25,000 ADU. As a result, the last several groups of these ramps get flagged by the jump step. The smaller number of groups used for these pixels in the ramp_fitting step results in them having larger read noise variances, which in turn leads to lower weights used during resampling. This ultimately leads to a lower than normal flux for the star in resampled images.
Once a group in a ramp has been flagged as affected by charge migration, all subsequent groups in the ramp are also flagged. By flagging these groups, they are not used in the computation of slopes in the ramp_fitting step. However, as described in the algorithm section below, they _are_ used in the calculation of the variance of the slope due to readnoise.
Input details¶
The input must be in the form of a RampModel
.
Algorithm¶
The first group, and all subsequent groups, exceeding the value of the
signal_threshold
parameter is flagged as CHARGELOSS. signal_threshold
is in units
of ADUs. These groups will also be flagged as DO_NOT_USE, and will not
be included in the slope calculation during the ramp_fitting
step. Despite being flagged
as DO_NOT_USE, these CHARGELOSS groups are still included in the calculation of the
variance due to readnoise.
This results in a readnoise variance for undersampled pixels that is similar to that of
pixels unaffected by charge migration. For the Poisson noise variance calculation in
ramp_fitting, the CHARGELOSS/DO_NOT_USE groups are not included.
For integrations having only 1 or 2 groups, no flagging will be performed.
Output product¶
The output is a new copy of the input RampModel
, with the updated DQ flags
added to the GROUPDQ array.