How Assumptions of the Underlying Spatial Profile Impact Extracted NIRISS/SOSS Spectra
Bibliographic record
Abstract
The James Webb Space Telescope (JWST) is set to revolutionize our understanding of universe. The future is particularly bright for exoplanetary astronomy, with the characterization of new worlds one of the primary goals of this extremely exciting telescope. Although each of the four instruments have observing modes tailored to time-series observations of exoplanets, the Single Object Slitless Spectroscopy (SOSS) mode of the NIRISS (Near-Infrared Imager and Slitless Spectrograph) instrument promises to be one of the workhorse observing modes; enabling unprecedented coverage of prominent water absorption bands as well as other critical constituents of exoplanet atmospheres, while also providing the bluest wavelength coverage of any instrument aboard the JWST. Along with these unique possibilities, the SOSS mode also presents astronomers with unique challenges – namely the contamination of the first two diffraction orders of the cross-dispersed spectrum on the SOSS detector. Recently Darveau-Bernier et al. (2022) developed the ATOCA algorithm to decontaminate the SOSS detector, allowing for extraction of the underlying spectrum from both the first and second diffraction orders. ATOCA models each pixel on the detector based on, amongst others, as assumption of the underlying spatial profiles of the first and second order traces. Here, we will present the effects on retrieved planetary parameters of a variety of assumptions during the reductions and 1d spectrum extraction of SOSS data. We address amongst other things, the effects of 1/f noise, as well as quantify the biases introduced by incorrect assumptions of the spectral trace profiles.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".