H-ATLAS: estimating redshifts of Herschel sources from sub-mm fluxes
Bibliographic record
Abstract
Upon its completion, the Herschel Astrophysics Terahertz Large Area Survey (H-ATLAS) will be the largest sub-millimetre survey to date, detecting close to half-a-million sources. It will only be possible to measure spectroscopic redshifts for a small fraction of these sources. However, if the rest-frame spectral energy distribution (SED) of a typical H-ATLAS source is known, this SED and the observed Herschel fluxes can be used to estimate the redshifts of the H-ATLAS sources without spectroscopic redshifts. In this paper, we use a sub-set of 40 H-ATLAS sources with previously measured redshifts in the range 0.5 < z < 4.2 to derive a suitable average template for high-redshift H-ATLAS sources. We find that a template with two dust components (Tc = 23.9 K, Th = 46.9 K and ratio of mass of cold dust to mass of warm dust of 30.1) provides a good fit to the rest-frame fluxes of the sources in our calibration sample. We use a jackknife technique to estimate the accuracy of the redshifts estimated with this template, finding a root mean square of Δz/(1 + z) = 0.26. For sources for which there is prior information that they lie at z > 1, we estimate that the rms of Δz/(1 + z) = 0.12. We have used this template to estimate the redshift distribution for the sources detected in the H-ATLAS equatorial fields, finding a bimodal distribution with a mean redshift of 1.2, 1.9 and 2.5 for 250, 350 and 500 μm selected sources, respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".