Quiescent or dusty? <i>Unveiling</i> the nature of extremely red galaxies at <i>z</i> &gt; 3
Bibliographic record
Abstract
ABSTRACT The advent of the JWST has revolutionized our understanding of high-redshift galaxies. In particular, the NIRCam instrument on-board JWST has revealed a population of red galaxies that had largely evaded detection with Hubble Space Telescope (HST), potentially due to significant dust obscuration, quiescence, or extreme redshift. Here, we present the first NIRSpec spectra of 23 red, HST faint or dark galaxies ($\mathrm{H-F444W\gt 1.75}$), unveiling their nature and physical properties. This sample includes both dusty and quiescent galaxies with spectroscopic data from NIRSpec/PRISM, providing accurate spectroscopic redshifts with $\mathrm{\overline{z}_{spec} = 4.1 \pm 0.7}$. The spectral features demonstrate that, while the majority of red galaxies are dusty, a substantial fraction, $\mathrm{13^{+9}_{-6} \%}$, are quiescent. For the dusty galaxies, we have quantified the dust attenuation using the Balmer decrement ($\mathrm{H\,\alpha / H\,\beta }$), finding attenuations $\mathrm{A_{V} \gt 2\ mag}$. We find that red dusty galaxies are $\mathrm{H\,\alpha }$ emitters with equivalent widths spanning the range $\mathrm{ 68 \mathring{\rm A}\lt EW_{H\alpha } \lt 550 \mathring{\rm A}}$, indicative of a wide range of recent star-formation activity. Whether dusty or quiescent, we find that red galaxies are predominantly massive, with 85 per cent of the galaxies in the sample having masses $\mathrm{log({\it M}_{*}/{\rm M}_{\odot }) \gt 9.8}$. This pilot NIRSpec programme reveals the diverse nature of HST-dark galaxies and highlights the effectiveness of NIRSpec/PRISM spectroscopic follow-up in distinguishing between dusty and quiescent galaxies and properly quantifying their physical properties. Upcoming research utilizing higher-resolution NIRSpec data and combining JWST with ALMA observations will enhance our understanding of these enigmatic and challenging sources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".