Simulating and interpreting deep observations in the Hubble Ultra Deep Field with the<i>JWST</i>/NIRSpec low-resolution ‘prism’
Bibliographic record
Abstract
The James Webb Space Telescope (JWST) will enable the detection of optical emission lines in galaxies spanning a broad range of luminosities out to redshifts |$z$| ≳ 10. Measurements of key galaxy properties, such as star formation rate and metallicity, through these observations will provide unique insight into, e.g. the role of feedback from stars and active galactic nuclei (AGNs) in regulating galaxy evolution, the co-evolution of AGNs and host galaxies, the physical origin of the ‘main sequence’ of star-forming galaxies, and the contribution by star-forming galaxies to cosmic reionization. We present an original framework to simulate and analyse observations performed with the near-infrared spectrograph (NIRSpec) on board JWST. We use the beagle tool (BayEsian Analysis of GaLaxy sEds) to build a semi-empirical catalogue of galaxy spectra based on photometric spectral energy distributions of dropout galaxies in the Hubble Ultra Deep Field (HUDF). We demonstrate that the resulting catalogue of galaxy spectra satisfies different types of observational constraints on high-redshift galaxies, and use it as an input to simulate NIRSpec/prism (R ∼ 100) observations. We show that a single ‘deep’ (∼100 ks) NIRSpec/prism pointing in the HUDF will enable |$\hbox{$\text {S}/\text {N}$}\gt 3$| detections of multiple optical emission lines in ∼30 (∼60) galaxies at |$z$| ≳ 6 (|$z \sim 4 \, \text {-}\, 6$|) down to |$\hbox{$m_\text {F160W}$}\lesssim 30$| AB mag. Such observations will allow measurements of galaxy star formation rates, ionization parameters, and gas-phase metallicities within factors of 1.5, mass-to-light ratios within a factor of 2, galaxy ages within a factor of 3, and V-band attenuation optical depths with a precision of 0.3.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".