Description

Class:

jwst.nsclean.NSCleanStep

Alias:

nsclean

Overview

The nsclean step applies an algorithm for removing correlated read noise from NIRSpec images. The noise often appears as faint vertical banding and so-called “picture frame noise.” The algorithm uses dark (unilluminated) areas of an image to fit a background model in Fourier space. When the fit is subtracted, it removes nearly all correlated noise. Compared to simpler strategies, like subtracting a rolling median, this algorithm is more thorough and uniform. It is also computationally undemanding, typically requiring only a few seconds to clean a full-frame image.

The correction can be applied to any type of NIRSpec exposure, including IFU, MOS, fixed slit, and Bright Object Time Series (BOTS), in both full-frame and subarray readouts. Time series (3D) data are corrected one integration at a time.

Note

The step is currently not capable of processing images taken using the “ALLSLITS” subarray. Other subarray types are allowed.

Details on the source of the correlated noise and the algorithm used in the nsclean step to fit and remove it can be found in Rauscher 2023.

Upon completion of the step, the step status keyword “S_NSCLEN” gets set to “COMPLETE” in the output science data.

Assumptions

As described below, the creation of a pixel mask depends on the presence of a World Coordinate System (WCS) object for the image, which is constructed by the assign_wcs step. In addition, creating a mask for IFU and MOS images depends on the presence of DQ flags assigned by the msaflagopen step. It is therefore required that those steps be run before attempting to apply nsclean.

Creation of an image mask

One of the key components of the correction is knowing which pixels are unilluminated and hence can be used in fitting the background noise. The step builds a mask on the fly for each image, which is used to mark useable and unuseable pixels. The mask is a 2D boolean array, having the same size as the image, with pixels set to True interpreted as being OK to use. The process of building the mask varies somewhat depending on the observing mode of the image being processed. Some features are common to all modes, while others are mode-specific. The following sections describe each type of masking that can be applied and at the end there is a summary of the types applied to each image mode.

The user-settable step parameter save_mask can be used to save the mask to a file, if desired (see nsclean step arguments).

Note that a user may supply their own mask image as input to the step, in which case the process of creating a mask is skipped. The step parameter user_mask is used to specify an input mask.

IFU Slices

For IFU images the majority of the mask is based on knowing which pixels are contained within the footprints of the IFU slices. To do this, the image’s World Coordinate System (WCS) object is queried in order to determine which pixels are contained within each of the 30 slices. Pixels within each slice are set to False (do not use) in the mask.

MOS/FS Slits

The footprints of each open MOS slitlet or fixed slit are flagged in a similar way as IFU slices. For MOS and FS images, the WCS object is queried to determine which pixels are contained within each open slit/slitlet and they are set to False in the mask.

MSA Failed Open Shutters

Pixels affected by stuck open MSA shutters are masked, because they may contain signal. This is accomplished by setting all pixels flagged by the msaflagopen step with DQ value “MSA_FAILED_OPEN” to False in the mask.

NaN Pixels

Any pixel in the input image that has a value of NaN is temporarily reset to zero for input to the fitting routine and flagged as False in the mask. Upon completion of the noise subtraction, this population of pixels is set back to NaN again in the output (corrected) image.

Fixed-Slit Region Pixels

Full-frame MOS and IFU images may contain signal from the always open fixed slits, which appear in fixed region in the middle of each image. The entire region containing the fixed slits is masked out when processing MOS and IFU images. The masked region is currently hardwired in the step to image indexes [1:2048, 923:1116], where the indexes are in x, y order and in 1-indexed values.

Left/Right Reference Pixel Columns

Full-frame images contain 4 columns of reference pixels on the left and right edges of the image. These are not to be used in the fitting algorithm and hence are set to False in the mask.

Outliers

Pixels in the unilluminated regions of the region can contain anomalous signal, due to uncaught Cosmic Rays, hot pixels, etc. A sigma-clipping routine is employed to find such outliers within the input image and set them to False in the mask. All pixels with values greater than \(median+n_sigma*sigma\) are set to False in the mask. Here median and sigma are computed from the image using the astropy.stats sigma_clipped_stats routine, using the image mask to exclude pixels that have already been flagged and a clipping level of 5 sigma. n_sigma is a user-settable step parameter, with a default value of 5.0 (see nsclean step arguments).

Mode-Specific Masking Steps

The following table indicates which flavors of masking are applied to images from each type of observing mode.

		Mode
Masking	IFU	MOS	FS
IFU Slices1	✓
Slits/Slitlets1		✓	✓
MSA_FAILED_OPEN	✓	✓	✓
NaN Pixels	✓	✓	✓
FS Region	✓	✓
Reference Pix	✓	✓	✓
Outliers	✓	✓	✓

¹The application of these steps can be turned on and off via the step parameter mask_spectral_regions. This parameter controls whether the “IFU Slices” and “Slits/Slitlets” portions of the masking are applied.