AMIGO: a Data-Driven Calibration of the JWST Interferometer

TL;DR

Amigo delivers an end-to-end differentiable calibration framework for JWST/NIRISS AMI that directly forward-models optics, detector physics, and readout, including the nonlinear brighter-fatter effect. By embedding a neural network within the detector ramp and coupling it with a physically grounded optical and visibility model, Amigo achieves robust, high-contrast detections (e.g., AB Dor AC and HD 206893 B/c) at separations near the diffraction limit, approaching photon-noise limits. The approach produces kernel-based observables (DISCO) that preserve information while mitigating instrument systematics and enables gradient-based Bayesian inference on astrophysical parameters. This framework, publicly available, paves the way for calibration of JWST and future missions with complex detector physics and non-ideal metrology, and suggests extensions to other instruments and deeper calibration data.

Abstract

The James Webb Space Telescope (JWST) hosts a non-redundant Aperture Masking Interferometer (AMI) in its Near Infrared Imager and Slitless Spectrograph (NIRISS) instrument, providing the only dedicated interferometric facility aboard - magnitudes more precise than any interferometric experiment previously flown. However, the performance of AMI (and other high resolution approaches such as kernel phase) in recovery of structure at high contrasts has not met design expectations. A major contributing factor has been the presence of uncorrected detector systematics, notably charge migration effects in the H2RG sensor, and insufficiently accurate mask metrology. Here we present Amigo, a data-driven calibration framework and analysis pipeline that forward-models the full JWST AMI system - including its optics, detector physics, and readout electronics - using an end-to-end differentiable architecture implemented in the Jax framework and in particular exploiting the dLux optical modelling package. Amigo directly models the generation of up-the-ramp detector reads, using an embedded neural sub-module to capture non-linear charge redistribution effects, enabling the optimal extraction of robust observables, for example kernel amplitudes and phases, while mitigating systematics such as the brighter-fatter effect. We demonstrate Amigo's capabilities by recovering the ABDor AC binary from commissioning data with high-precision astrometry, and detecting both HD206893B and the inner substellar companion HD206893c: a benchmark requiring contrasts approaching 10 magnitudes at separations of only 100 mas. These results exceed outcomes from all published pipelines, and re-establish AMI as a viable competitor for imaging at high contrast at the diffraction limit. Amigo is publicly available as open-source software community resource.

Figures (21)

• Figure 1: Left panel: Schematic diagram of the 7-hole nrm projected over the primary mirror. Middle panel: The resulting psf (i.e. interferogram) from the non-redundant mask, visualised on a square-root scale to highlight low-power features. Right panel: The power-spectrum of the psf featuring baseline-specific regions of fringe power known in the literature as splodges that can be conveniently found by Fourier transform of the psf. The 21 discrete non-redundant baselines are indicated by the overlaid green dots.
• Figure 2: High level flow diagram of the amigo model and pipeline, showing the input and output product and shapes passed between each modular component. n$\lambda$ is the number of wavelengths modelled by the optics, n$_g$ is the number of groups in the data, and n$_{ints}$ is the number of integrations. Each of these model and pipeline components are discussed in detail in their own section.
• Figure 3: Residuals from second-order polynomial fits to ramp data, shown before (top row) and after (bottom row) applying a sine-wave-based correction for adc integral non-linearity. The left panels plot residuals as a function of the ramp value, while the right panels show the same residuals folded over a modulo 1024 pattern, revealing periodic structure. Prior to correction, the residuals exhibit a strong sinusoidal modulation. The applied correction consists of a 1024-period sine wave with fixed amplitude, significantly reducing both the overall residual structure and the folded periodicity (bottom panels). Orange points and error bars represent binned data mean and standard error in each bin, highlighting the improved uniformity of residuals post-correction.
• Figure 4: Left panel: Residual between the calibrated aperture mask and its idealised undistorted counterpart. Right panel: psf residuals of the four primary optical effects on the psf. Top left: Instrumental jitter, applied through a convolution with a Gaussian kernel. Top right: Primary mirror aberrations, modelled using Zernike polynomials on the primary. Bottom left: Aperture mask distortions, modelled by applying a distortion to the coordinates over which the aperture mask is calculated. Bottom right: Fresnel defocus modelled using a Fresnel propagation algorithm. All effects are shown for the F430M filter, using a psf with $10^6$ total photons.
• Figure 5: Flow chart of the injection of visibility signals to forwards-modelled psfs. This demonstrates how high-resolution visibility signals can be directly injected into any psf model provided the appropriate set of visibility basis vectors. An example binary-star signal is injected as a demonstrator.