Breaking the Redshift Deadlock - I: Constraining the star formation history of galaxies with sub-millimetre photometric redshifts
Bibliographic record
Abstract
Future extragalactic sub-millimetre and millimetre surveys have the potential to provide a sensitive census of the level of obscured star formation in galaxies at all redshifts. While in general there is good agreement between the source counts from existing SCUBA (850um) and MAMBO (1.25mm) surveys of different depths and areas, it remains difficult to determine the redshift distribution and bolometric luminosities of the sub-millimetre and millimetre galaxy population. This is principally due to the ambiguity in identifying an individual sub-millimetre source with its optical, IR or radio counterpart which, in turn, prevents a confident measurement of the spectroscopic redshift. Additionally, the lack of data measuring the rest-frame FIR spectral peak of the sub-millimetre galaxies gives rise to poor constraints on their rest-frame FIR luminosities and star formation rates. In this paper we describe Monte-Carlo simulations of ground-based, balloon-borne and satellite sub-millimetre surveys that demonstrate how the rest-frame FIR-sub-millimetre spectral energy distributions (250-850um) can be used to derive photometric redshifts with an r.m.s accuracy of +/- 0.4 over the range 0 < z < 6. This opportunity to break the redshift deadlock will provide an estimate of the global star formation history for luminous optically-obscured galaxies [L(FIR) > 3 x 10^12 Lsun] with an accuracy of 20 per cent.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".