The bright extragalactic ALMA redshift survey (BEARS) – II. Millimetre photometry of gravitational lens candidates
Bibliographic record
Abstract
ABSTRACT We present 101- and 151-GHz ALMA continuum images for 85 fields selected from Herschel observations that have 500-μm flux densities >80 mJy and 250–500-μm colours consistent with z > 2, most of which are expected to be gravitationally lensed or hyperluminous infrared galaxies. Approximately half of the Herschel 500-μm sources were resolved into multiple ALMA sources, but 11 of the 15 brightest 500-μm Herschel sources correspond to individual ALMA sources. For the 37 fields containing either a single source with a spectroscopic redshift or two sources with the same spectroscopic redshift, we examined the colour temperatures and dust emissivity indices. The colour temperatures only vary weakly with redshift and are statistically consistent with no redshift-dependent temperature variations, which generally corresponds to results from other samples selected in far-infrared, submillimetre, or millimetre bands but not to results from samples selected in optical or near-infrared bands. The dust emissivity indices, with very few exceptions, are largely consistent with a value of 2. We also compared spectroscopic redshifts to photometric redshifts based on spectral energy distribution templates designed for infrared-bright high-redshift galaxies. While the templates systematically underestimate the redshifts by ∼15 per cent, the inclusion of ALMA data decreases the scatter in the predicted redshifts by a factor of ∼2, illustrating the potential usefulness of these millimetre data for estimating photometric redshifts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".