APPLESOSS: A Producer of ProfiLEs for SOSS. Application to the NIRISS SOSS Mode
Bibliographic record
Abstract
Abstract The SOSS mode of the Near Infrared Imager and Slitless Spectrograph instrument is poised to be one of the workhorse modes for exoplanet atmosphere observations with the newly launched James Webb Space Telescope (JWST). One of the challenges of the SOSS mode, however, is the physical overlap of the first two diffraction orders of the G700XD grism on the detector. Recently, the ATOCA algorithm was developed and implemented as an option in the official JWST pipeline, as a method to extract SOSS spectra by decontaminating the detector—that is, separating the first and second orders. Here, we present A Producer of ProfiLEs for SOSS (APPLESOSS), which generates the spatial profiles for each diffraction order upon which ATOCA relies. We validate APPLESOSS using simulated SOSS time series observations of WASP-52 b, and compare it to ATOCA extractions using two other spatial profiles (a best and worst case scenario on-sky), as well as a simple box extraction performed without taking into account the order contamination. We demonstrate that APPLESOSS profiles retain a high degree of fidelity to the true underlying spatial profiles, and therefore yield accurate extracted spectra. We further confirm that the effects of the order contamination for relative measurements (e.g., exoplanet transmission or emission observations) is small—the transmission spectrum obtained from each of our four tests, including the contaminated box extraction, is consistent at the ∼1σ level with the atmosphere model input into our noiseless simulations. We further confirm via a retrieval analysis that the atmosphere parameters (metallicity and C/O) obtained from each transmission spectrum are consistent with the true underlying values.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".