JWST Aperture Masking Interferometry Pipeline Caveats

Content Migration

WARNING: This page has been deprecated and the content migrated to NIRISS AMI Known Issues.  Please update your links accordingly.

Reduction and calibration of data from the NIRISS aperture masking interferometry (AMI) observing mode, using the JWST Science Calibration Pipeline and stand-alone code to extract interferometric observables, is described in this article. 

On this page

Summary of specific AMI issues

The information in this table about NIRISS AMI calibration pipeline issues is excerpted from Known Issues with JWST Data Products.

SymptomsCauseWorkaroundMitigation 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.

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. STScI will reprocess affected data products with an updated Operations Pipeline in 2024 (date TBD). Reprocessing of affected data typically takes 2–4 weeks after the update.

Observations with the aperture masking interferometry (AMI) mode

See also: NIRISS Aperture Masking InterferometryNIRISS Non-Redundant Mask

NIRISS's aperture masking interferometry (AMI) mode (Monnier 2003) turns the full aperture of JWST into an interferometric array. Light admitted by 7 holes or sub-apertures in an otherwise opaque pupil mask interferes to produce an interferogram on the detector. The mask is designed such that each baseline (i.e., the vector linking the centers of 2 holes) is unique and forms fringes with a unique spatial frequency in the image plane. Since each spatial frequency is sampled only once, the mask is called "non-redundant." (A full aperture, on the other hand, can be considered as made up of infinite sub-apertures and multiple sets of sub-apertures to generate the same baseline making the aperture extremely redundant.) The interferogram created by the aperture mask has a sharper core than that provided by normal "direct" imaging.

The advantage is significant: while the ability to separate closely spaced objects with normal imaging is given by the familiar Rayleigh criterion (separation \delta\theta = 1.22 \, \lambda / D, where $\lambda$ is the wavelength of light and D is the diameter of the telescope), interferometry can resolve objects as close as \delta\theta = 0.5 \, \lambda / D (the Michelson criterion). The AMI mode allows planetary or stellar companions that are up to ~9 magnitudes fainter than their host star and separated by ~70–400 mas to be detected and characterized. It can also be used to reconstruct high resolution maps of extended sources, such as active galactic nuclei.

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 (version 1.8) 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.

Pipeline stages and steps

See also: calwebb_detector1calwebb_image2JWST Data Structures

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. The calwebb_image2 stage of the pipeline corrects for flat field effects and creates calibrated image files. When processing AMI data through the calwebb_detector1 stage, the IPC correction should be turned off. 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.

The "*_calints.fits" files from the calwebb_image2 stage of the pipeline can then be processed with the bad pixel correction code and analyzed with an observable extraction code such as ImPlaneIA.

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 to apply the bad pixel correction to pipeline-calibrated FITS files before the corrected files are further analyzed with ImPlaneIA

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)

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


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

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

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

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

Latest updates
Originally published