Outlier Detection for IFU Data

This module serves as the interface for applying outlier_detection to IFU observations, like those taken with NIRSpec and MIRI. The code implements the basic outlier detection algorithm searching for pixels that are consistent outliers in the calibrated images created by the calwebb_spec2 pipeline. After launch it was discovered the bad pixels on the MIRI detectors vary with time. The pixels varied from usable to unusable, and at times, back to usable on a time frame that was too short (sometimes as short as 2 days) to fold into the bad pixel mask applied in the calwebb_detector1 pipeline. At this time it is believed that NIRSpec IFU data also have bad pixels that vary with time, though the time variation is still under study.

An algorithm was developed to flag pixels that are outliers when compared to their neighbors for a set of input files contained in an association. The neighbor pixel differences are the neighbors in spatial direction. For MIRI data ,the neighbor differences are found to the left and right of every science pixel. While for NIRSpec data neighbor differences are found between the pixels above and below every science pixel. The pixel differences for each input model in the association is determined and is stored in a stack of pixel differences. For each pixel the minimum difference through this stack is determined and normalized. The normalization uses a local median of the difference array (set by the kernel size). A pixel is flagged as an outlier if this normalized minimum difference is greater than the input threshold percentage. Pixels that are found to be outliers are flaged in in the DQ array.

jwst.outlier_detection.outlier_detection_ifu Module

Class definition for performing outlier detection on IFU data.

Classes

OutlierDetectionIFU(input_models[, reffiles])

Sub-class defined for performing outlier detection on IFU data.

Class Inheritance Diagram