NIRISS AMI Known Issues

Known issues specific to NIRISS AMI data processing in the JWST Science Calibration Pipeline are described in this article. This is not intended as a how-to guide or as full documentation of individual pipeline steps, but rather to give a scientist-level overview of issues that users should be aware of for their science. 

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Specific artifacts are described in the Artifacts section below. Guidance on using the pipeline data products is provided in the Pipeline Notes section along with a summary of some common issues and workarounds in the summary section.

Please also refer to NIRISS AMI Calibration Status for an overview of the current astrometric, photometric, and target acquisition accuracy of NIRISS AMI data products.



Artifacts

Information on NIRISS instrument artifacts are found on the main NIRISS Known Issues page.



Pipeline notes

Analysis of AMI data involves extraction of the interferometric observables, including the fringe phases and amplitudes, to reconstruct a model of the host system where the faint companion can be identified and characterized. Programs with AMI normally require observations of the science target and a nearby PSF reference star for calibration. Both the AMI science target and PSF reference star calibrator(s) must be processed through the JWST Science Calibration Pipeline.

Words in bold are GUI menus/
panels or data software packages; 
bold italics are buttons in GUI
tools or package parameters.

Although the current version of the JWST Science Calibration Pipeline correctly processes AMI data through the calwebb_image2 stage, the quality of the products generated by the calwebb_ami3 stage require further evaluation. In the meantime, the "Image Plane approach to Interferometric Analysis (ImPlaneIA, Greenbaum et al. 2015), provides robust and rigorously tested software to complete the analysis of AMI data. ImPlaneIA and two other equivalent interferometric extraction codes are described in Sivaramakrishnan et al. 2023 and compared in Lau et al. 2023.

calwebb_detector1

AMI data can be processed through the calwebb_detector1 stage of the pipeline, which corrects for detector effects like dark current and identifies bad pixels. When processing AMI data through the calwebb_detector1 stage, the IPC correction should be turned off, which it is by default.

Bad pixel correction

The pipeline flags bad pixels in the calwebb_detector1 stage of the pipeline using the bad pixel mask reference file. Simply masking these pixels is insufficient for AMI: the bad pixels need to be corrected in the data in order to accurately centroid the image for image-plane observable extraction. The bad pixel correction code uses the pupil geometry to identify noise in the image and correct pixel values that have unphysical signal. The method is described in Ireland 2013 and implemented in Kammerer et al. 2019. This code can be used on the calwebb_image2 data products (see below) to apply the bad pixel correction to pipeline-calibrated FITS files before the corrected files are further analyzed. Note that this bad pixel correction algorithm will be implemented as part of the updated calwebb_ami3 stage which is estimated to be released in version 1.14.

calwebb_image2

 The calwebb_image2 stage of the pipeline corrects for flat field effects and creates calibrated image files. When processing AMI data through the calwebb_image2 stage of the pipeline, the photom and resample steps should be turned off, which prevents a photometric calibration from being applied and the images from being resampled onto a common astrometric grid. For the AMI mode, the photom and resample steps are turned off by default when in the MAST data products processed by the JWST pipeline. When processing data with the JWST pipeline in Python, the .call() method will apply these defaults but the .run() method will not since the .run() method is designed to only apply user-specified values. To turn off the photom and resample steps of the calwebb_image2 stage when running the JWST pipeline in python using the .run() method, these options have to be passed explicitly, i.e., photom.skip=True and resample.skip = True. 

From the calwebb_image2 stage of the pipeline, the "*_calints.fits" files, which are 3-D data products (Nintegrations × Nrows × Ncolumns),  can then be processed with the bad pixel correction code and further analyzed.

calwebb_ami3

For pipeline versions 1.12.3 and earlier, an older algorithm to extract interferometric observables is implemented in calwebb_ami3 (see known issue NR-AMI01 in the table below). Implementation of a more state-of-the-art algorithm into calwebb_ami3, using ImPlaneIA (see below), is in development and estimated to be released in pipeline version 1.14. Users are currently advised to run ImPlaneIA off-line on the bad pixel corrected calibrated data products from the calwebb_image2 stage of the pipeline.

Image Plane approach to Interferometric Analysis (ImPlaneIA)

ImPlaneIA extracts interferometric observables from each integration of the bad pixel-corrected calibrated data, and stores the results in OIFITS2 (Optical Interferometry FITS) format. ImPlaneIA reduces aperture masking images by fitting an analytical model to the data to extract interferometric observables such as fringe phases and fringe amplitudes, closure phases and closure amplitudes and pistons (optical path delays between mask holes).

After generating OIFITS files for both the source and calibrator star, the instrumental contribution to the closure phases and fringe amplitudes need to be removed. This is done by using the calibrate_oifits routine from ImPlaneIA on the source and calibrator OIFITS files to calibrate the target. This routine will return a calibrated OIFITS file that can be used with community-developed analysis software like CANDID (Gallenne et al. 2015) or Fouriever to extract binary parameters, or an image reconstruction code like SQUEEZE (Baron et al. 2010) or BSMEM (Skilling & Bryan 1984, Buscher 1994, Baron & Young 2008)



Summary of common issues and workarounds

The sections above provide detail on each of the known issues affecting NIRISS AMI data; the table below summarizes some of the most likely issues users may encounter along with any workarounds if available. Note that greyed-out issues have been retired, and are fixed as of the indicated pipeline build.

SymptomsCauseWorkaroundFix BuildMitigation Plan

NR-AMI01: Interferometric properties returned by the calwebb_ami3 stage of the pipeline require further evaluation. Data are served in FITS files rather than the interferometric convention to use OIFITS formatted files.

The Science Calibration Pipeline is using an older version of the image plane reconstruction software.

The JWST Aperture Masking Interferometry Pipeline Caveats article provides descriptions of how to analyze data products off-line after the calwebb_image2 stage of the Science Calibration Pipeline, with links to analysis tools.

N/A

Updated issue

The calwebb_ami3 stage of the Science Calibration Pipeline is currently being updated to use state-of-the-art code and to serve OIFITS formatted files. The fix is planned to become public with the Operations Pipeline build planned for installation in early 2024. Reprocessing of affected data typically takes 2–4 weeks

NR-AMI02: When the peak pixel of a PSF in AMI mode reaches beyond ~25,000 ADU in an integration, it starts "spilling" charge to its neighboring pixels, thus causing an effective "widening" of the PSF.

This is due to the so-called "brighter-fatter effect" (BFE) that affects near-IR H2RG detectors. 

No generic workaround is available until an Operations Pipeline build planned for installation in November 2023. For advice on specific datasets for which the peak intensity reaches beyond ~25,000 ADU per integration, a help desk ticket can be submitted. The NIRISS AMI Recommended Strategies article in JDox describes how to avoid this problem at the proposal planning stage. 

Updated issue

Apply a new Science Calibration Pipeline step called charge_migration within the calwebb_detector1 stage (Goudfrooij et al. 2024). STScI is reprocessing affected data products with an updated Operations pipeline, installed on December 5, 2023.  Reprocessing of affected data typically takes 2–4 weeks after the update.



Supporting links

Bad pixel correction code (github link)

Companion Analysis and Non-Detection in Interferometric Data (CANDID) github page

Fouriever github page

Image Plane approach to Inteferometric Analysis (ImPlaneIA) github page

SQUEEZE github page



References

Baron, F., Monnier, J. D., Kloppenborg, B., 2010, SPIE, 7734, 21
A novel image reconstruction software for optical/infrared interferometry

Baron, F. Young, J. S., 2008, SPIE, 7013E, 3XB
Image reconstruction at Cambridge University

Buscher, D. F., 1994, IAUS, 158, 91B
Direct maximum-entropy image reconstruction from the bispectrum

Gallenne, A., Mérand, A., Kervella, P., et al., 2015, A&A, 579, 68
Robust high-contrast companion detection from interferometric observations. The CANDID algorithm and an application to six binary Cepheids

Goudfrooij, P. et al. 2024, PASP, 136, 4503
An Algorithm to Mitigate Charge Migration Effects in Data from the Near Infrared Imager and Slitless Spectrograph on the James Webb Space Telescope

Greenbaum, A., Pueyo, L., Sivaramakrishnan, A., Lacour, S., et al., 2015, ApJ, 798, 68
An Image-plane Algorithm for JWST's Non-redundant Aperture Mask Data

Ireland, M. J, 2013, MNRAS, 433, 1718
Phase errors in diffraction-limited imaging: contrast limits for sparse aperture masking

Kammerer, J., Ireland, M. J., Martinache, F., et al. 2019, MNRAS, 486, 639
Kernel phase imaging with VLT/NACO: high-contrast detection of new candidate low-mass stellar companions at the diffraction limit

Lau, R. M. et al. 2023 arXiv:2311:15948
A First Look with JWST Aperture Masking Interferometry (AMI): Resolving Circumstellar Dust around the Wolf-Rayet Binary WR 137 beyond the Rayleigh Limit

Monnier J. D. 2003, Reports on Progress in Physics, 66, 789 
Optical interferometry in astronomy

Sivaramakrishnan, A. et al. 2023, PASP, 135, 1043
The Near Infrared Imager and Slitless Spectrograph for the James Webb Space Telescope. IV. Aperture Masking Interferometry

Skilling, J., Bryan, R. K., 1984, MNRAS, 211, 111
Maximum Entropy Image Reconstruction - General Algorithm



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Originally published