ATOCA: an Algorithm to Treat Order Contamination. Application to the NIRISS SOSS Mode
Bibliographic record
Abstract
Abstract After a successful launch, the James Webb Space Telescope is preparing to undertake one of its principal mission objectives, the characterization of the atmospheres of exoplanets. The Single Object Slitless Spectroscopy (SOSS) mode of the Near Infrared Imager and Slitless Spectrograph (NIRISS) is the only observing mode that has been specifically designed for this objective. It features a wide simultaneous spectral range (0.6–2.8 μm) through two spectral diffraction orders. However, due to mechanical constraints, these two orders overlap slightly over a short range, potentially introducing a “contamination” signal in the extracted spectrum. We show that for a typical box extraction, this contaminating signal amounts to 1% or less over the 1.6–2.8 μm range (order 1), and up to 1% over the 0.85–0.95 μm range (order 2). For observations of exoplanet atmospheres (transits, eclipses or phase curves) where only temporal variations in flux matter, the contamination signal typically biases the results by order of 1% of the planetary atmosphere spectral features strength. To address this problem, we developed the Algorithm to Treat Order ContAmination (ATOCA). By constructing a linear model of each pixel on the detector, treating the underlying incident spectrum as a free variable, ATOCA is able to perform a simultaneous extraction of both orders. We show that, given appropriate estimates of the spatial trace profiles, the throughputs, the wavelength solutions, as well as the spectral resolution kernels for each order, it is possible to obtain an extracted spectrum accurate to within 10 ppm over the full spectral range.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".