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Mitigating Resampling Artifacts for the JWST IFU Spectrometers with Adaptive Trace Modeling

This paper presents a generalized technique using cubic basis spline models to adaptively interpolate spectral traces onto a higher-resolution grid before rectification, effectively mitigating resampling artifacts and reducing biases in James Webb Space Telescope IFU data across diverse observational scenarios.

Original authors: David R. Law, Melanie Clarke

Published 2026-03-31

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CC BY 4.0

Original authors: David R. Law, Melanie Clarke

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The Big Problem: The "Staircase" Effect

Imagine you are trying to take a photo of a perfectly smooth, curved rainbow using a camera that only has a very low-resolution grid of square pixels. Because the rainbow is so sharp and the pixels are so big, the camera can't capture the smooth curve. Instead, it has to guess where the color goes.

If the rainbow is perfectly aligned with the grid, the photo looks okay. But if the rainbow is tilted just slightly, the camera has to guess wildly. One pixel might catch a bright spot, the next might catch a shadow, and the next might catch a bright spot again. When you try to stitch these photos together or zoom in, you don't see a smooth rainbow; you see a jagged, wiggly "staircase" pattern.

In the real world:
The James Webb Space Telescope (JWST) is so powerful that it sees details that are smaller than the individual "pixels" on its cameras. This is called undersampling. When astronomers try to combine multiple images (a process called "resampling" or "drizzling") to make a clean 3D map of the universe, this mismatch creates fake, wiggly lines in the data. These aren't real stars or gas; they are just digital artifacts, like static on an old TV.

The Old Way: Fixing the Mess After It Happens

Previously, scientists tried to fix these wiggles after the data was already scrambled.

The Analogy: Imagine you baked a cake, but you forgot to mix the sugar in, so it's lumpy. The old method was to take a knife and try to shave off the lumps after the cake was baked. It worked sometimes, but you risked cutting off real chocolate chips (real scientific data) along with the lumps.
The Paper's Critique: The authors say this is risky. If you try to mathematically "smooth out" the wiggles, you might accidentally smooth out a real, faint signal you were looking for.

The New Solution: The "Adaptive Trace Model" (ATM)

The authors, David Law and Melanie Clarke, propose a smarter way: Don't let the mess happen in the first place.

Instead of taking the low-res photo and trying to fix it later, they build a "smart guess" of what the smooth curve should look like before they take the photo.

Here is how their method works, broken down into three steps:

1. The "Linear Sketch" (The Rough Draft)

First, they take the raw data and do a simple, straight-line connection between the pixels.

Analogy: This is like connecting the dots on a "connect-the-dots" puzzle with a ruler. It's fast and okay for smooth areas, but it still looks a bit blocky around the edges.

2. The "Spline Model" (The Smart Curve)

This is the magic part. The telescope knows how light behaves. It knows that a star's light should be a smooth, round blob (a Point Spread Function).

The Trick: The computer looks at the light from the star across many different colors (wavelengths). Even though the star is tilted on the grid, the computer realizes, "Hey, at this specific angle, the light is hitting the edge of a pixel. But at the next angle, it's hitting the center."
The Analogy: Imagine you are trying to trace a circle by looking at it through a picket fence. You can't see the whole circle at once. But if you move your head slightly left and right, you see different parts of the circle peeking through the slats. By combining all those glimpses, you can draw a perfect circle, even though your view was blocked by the fence.
The Result: The computer uses a "cubic spline" (a fancy mathematical curve) to draw that perfect circle, filling in the gaps between the pixels with high precision.

3. The "Residual" (The Safety Net)

What if the object isn't a simple star? What if it's a star inside a messy cloud of gas? The "smart curve" might try to smooth out a real, jagged gas cloud.

The Fix: The computer calculates the difference between the "smart curve" and the actual raw data. If there is a real, jagged feature (like a gas cloud) that the curve missed, it adds that difference back in.
Analogy: It's like drawing a perfect circle on a piece of paper, but then realizing there was a smudge of ink on the paper that wasn't part of the circle. You add the smudge back in so the final picture is accurate.

Why This Matters

The authors tested this on real JWST data (looking at stars, black holes, and baby stars).

Before: The data looked like a wiggly, noisy mess. Real details were hidden behind the digital static.
After: The wiggles disappeared. The data became smooth and clean.
The Benefit: Astronomers can now look at tiny, crowded areas of the sky and see faint details that were previously buried in the noise. They can study the chemistry of baby stars or the gas swirling around black holes with much higher confidence.

The Bottom Line

The authors created a "turn-key" tool (a ready-to-use software) that is now part of the official JWST data processing pipeline.

Think of it this way:
If the old way was trying to fix a blurry photo with Photoshop, the new way is like putting a high-definition lens on the camera before you take the picture. It uses the physics of light and the geometry of the telescope to predict exactly where the light should be, ensuring that when we look at the universe, we see it clearly, not through a digital filter of our own making.

1. The Problem: Undersampling and Resampling Artifacts

The James Webb Space Telescope (JWST) Integral Field Unit (IFU) spectrometers (NIRSpec and MIRI MRS) operate at or near the diffraction limit. Consequently, the Point Spread Function (PSF) is often undersampled by the detector pixels (fewer than 2 pixels per FWHM).

The Core Issue: When data from dithered observations are combined into regular, rectified data cubes (a process known as "drizzling"), the undersampling causes resampling artifacts.
Manifestation: These artifacts appear as low-frequency, oscillating spectral features (sinusoidal patterns) in the extracted spectra.
Cause: The artifacts arise because the dispersed spectral trace is tilted relative to the detector pixel grid. As wavelength changes, the PSF "phase" shifts across pixel boundaries. If a PSF peak falls between pixels in one exposure and on a pixel in another, simple resampling creates artificial dips or peaks.
Impact: These artifacts are most severe in single-spaxel spectra near unresolved point sources, complicating scientific analysis such as kinematic mapping, line emission fitting, and outlier detection.
Limitations of Current Solutions: Existing methods (e.g., sinusoidal modeling, Fourier analysis) attempt to remove artifacts after they have been introduced into the data cube. These require extensive customization and risk biasing genuine spectral features.

2. Methodology: Adaptive Trace Modeling (ATM)

The authors propose a pre-rectification technique called Adaptive Trace Modeling (ATM). Instead of correcting the final cube, ATM interpolates the observed spectral traces onto a higher-resolution grid before the data cube is constructed.

The algorithm operates on the native calibrated detector images (forig) and constructs a model (finterp) using three components:

A. Preprocessing

Calculates sigma-clipped mean and RMS to handle calibration offsets (e.g., dark current oversubtraction).
Masks pixels associated with specific IFU slices.

B. Three-Step Modeling Process

The method constructs a final oversampled image by combining three intermediate models:

Linear Model ( $f_{linear}$ ):
- A simple column-by-column linear interpolation of the data onto a grid with $N$ times higher spatial sampling (e.g., $N=2$ or $3$).
- Role: Provides an unbiased baseline. It is sufficient for smooth regions but does not fully resolve undersampling issues near PSF peaks.
Spline Model ( $f_{spline}$ ):
- Uses cubic basis splines to model the 1D spatial profile along the slice.
- Adaptive Strategy: It leverages the fact that the PSF shape changes slowly with wavelength. By normalizing and combining data from adjacent columns (wavelengths), it creates a high-sampling profile with different pixel phases.
- Knot Selection: The number of spline knots is tuned (e.g., ~68 for NIRSpec, ~36 for MIRI) to balance fitting real structure without overfitting noise.
- Limitation: The spline assumes a slowly varying profile. It can artificially suppress narrow, wavelength-dependent features (e.g., emission lines filling the field) or complex structures from multiple sources.
Residual Model ( $f_{resid}$ ):
- To correct for the spline's limitations, the authors calculate the difference between the original data and the scaled spline model.
- This residual image is oversampled using the linear technique and added back to the spline model.
- Role: Captures narrow spectral features, bad pixel clusters, and complex structures that the smooth spline model misses.

C. Hybrid Application

The final oversampled image ( $f_{interp}$ ) is a hybrid:

High-Slope Regions: Where the spectral trace crosses pixel boundaries rapidly (indicating severe undersampling), the Spline + Residual model is used.
Low-Slope Regions: Where sampling is adequate, the Linear model is used for computational efficiency.

3. Key Contributions

Pre-emptive Correction: Unlike previous methods that model artifacts after resampling, ATM prevents the artifacts from forming by reconstructing the trace at a higher resolution before cube construction.
Data-Driven & Tunable: The method does not require a priori knowledge of the JWST PSF. It is fully data-driven, making it applicable to arbitrary scenes (point sources, extended nebulae, crowded fields).
Handling Bad Pixels: The spline model effectively interpolates across clusters of bad pixels or cosmic rays, outperforming standard pipeline pixel-replacement steps which often fail on larger gaps.
Implementation: The algorithm has been integrated as an optional step (ADAPTIVE TRACE MODEL) in the JWST calibration pipeline (Build 12.3).

4. Results and Performance

The authors validated the technique using standard stars (J1743045, HD 2811) and science targets (QSO LBQS 0302-0019).

Artifact Reduction:
- NIRSpec: For single-spaxel spectra, resampling artifact amplitudes dropped from ~6–9% (standard cubes) to ~1–3% with $N=3$ oversampling.
- MIRI MRS: Artifacts (compounded by instrumental fringing) were reduced from ~7–10% to ~1–2%.
Flux Conservation: Summed spectra over apertures remained consistent with the original data to within 0.1–0.3%, confirming no significant flux loss.
Recovery of Features: In the QSO test case, faint emission lines (e.g., He I, H $\delta$ ) that were obscured by noise in standard cubes became clearly visible in ATM-corrected spaxel spectra.
Computational Cost:
- Runtime increases moderately. For NIRSpec, creating spline models adds ~12 seconds per slice.
- A typical exposure takes ~90 seconds (simple) to ~6 minutes (complex) to process.
- Subsequent pipeline steps (Pixel Replace, Cube Build) see runtime increases of 2–3x due to the larger number of input pixels, but this is considered acceptable for the gain in data quality.

5. Significance

This technique represents a paradigm shift in handling JWST IFU data. By moving the correction to the detector level, it allows astronomers to:

Analyze Single Spaxels: Extract scientifically robust spectra from individual spaxels without the need for large extraction apertures that blur spatial resolution.
Study Complex Scenes: Accurately disentangle point sources from extended emission (e.g., AGN in host galaxies) without the confusion of resampling artifacts.
Improve Reliability: Reduce the need for post-hoc artifact modeling, thereby reducing the risk of biasing scientific results.

The authors conclude that ATM is a "turn-key" solution that significantly enhances the scientific return of JWST IFU observations, particularly for high-precision spectroscopy of crowded or complex fields.