Understanding galaxy formation and evolution through an all-sky submillimetre spectroscopic survey
Bibliographic record
Abstract
We illustrate the extraordinary discovery potential for extragalactic astrophysics of a far-IR/submm all-sky spectroscopic survey with a 3m-class space telescope. Spectroscopy provides both a 3D view of the Universe and allows us to take full advantage of the sensitivity of present-day instrumentation, overcoming the spatial confusion that affects broadband far-IR/submm surveys. Emission lines powered by star formation will be detected in galaxies out to $z \simeq 8$. It will provide measurements of spectroscopic redshifts, SFRs, dust masses, and metal content for millions of galaxies at the peak epoch of cosmic star formation and of hundreds of them at the epoch of reionization. Many of these galaxies will be strongly lensed; the brightness amplification and stretching of their sizes will make it possible to investigate (by means of follow-up with high-resolution instruments) their internal structure and dynamics on the scales of giant molecular clouds. This will provide direct information on the physics driving the evolution. Furthermore, the arc-min resolution of the telescope at submm wavelengths is ideal for detecting the cores of galaxy proto-clusters, out to the epoch of reionization. Tens of millions of these galaxy-clusters-in-formation will be detected at $z \simeq 2$-3, with a tail out to $z \simeq 7$, and thousands of detections at 6 < z < 7. Their study will allow us to track the growth of the most massive halos well beyond what is possible with classical cluster surveys (mostly limited to $z < 1.5$-2), tracing the history of star formation in dense environments and teaching us how star formation and galaxy-cluster formation are related across all epochs. Such a survey will overcome the current lack of spectroscopic redshifts of dusty star-forming galaxies and galaxy proto-clusters, representing a quantum leap in far-IR/submm extragalactic astrophysics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".