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. 

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.

Observations with the aperture masking interferometry (AMI) mode

See also: NIRISS Aperture Masking Interferometry, NIRISS 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 $//<![CDATA[ \begin{array}{l}\delta\theta = 1.22 \, \lambda / D\end{array} //]]>$, where $//<![CDATA[ \begin{array}{l}\lambda\end{array} //]]>$ is the wavelength of light and D is the diameter of the telescope), interferometry can resolve objects as close as $//<![CDATA[ \begin{array}{l}\delta\theta = 0.5 \, \lambda / D\end{array} //]]>$ (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.

Pipeline stages and steps

See also: calwebb_detector1, calwebb_image2, JWST 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

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

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