Planning for Roman: Case Study of Wide-Field Slitless Grism Spectroscopy with JWST
Bibliographic record
Abstract
The Roman Wide-Field Slitless Grism Spectrometer aims at measuring redshifts and emission line properties of tens of millions of galaxies over a significant portion of the extragalactic sky. By doing so, it will provide the most precise mapping of matter clustering over such a large area, enabling the study of the Universe’s expansion and of the structure and distribution of baryons and dark matter through cosmic time in unprecedented details. A small number of teams have now published the first results using JWST’s slitless grism capabilities provided by the NIRISS instrument. In particular among them, a team of researchers have made the first thorough inspection and characterization of all of the grism spectroscopic data observed over an entire deep NIRISS pointing: that of the Early Release Observation of Webb’s First Deep Field, SMACS J0723.3-7327. In this talk, I will review these first JWST results using wide-field slitless grism spectroscopic data and present the many challenges of obtaining precise spectroscopic redshifts from such observations. I will also present current solutions implemented within the JWST-GTO Canadian NIRISS Unbiased Cluster Survey (CANUCS) program to overcome these challenges, offering perspectives for current and future wide-field slitless grism spectroscopic programs and observations.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 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".